From f51834514df264da13d52f0a594ce0e8df4a5228 Mon Sep 17 00:00:00 2001 From: Phillip Chlap Date: Wed, 26 May 2021 06:13:56 +0000 Subject: [PATCH] Work on docs --- .vscode/settings.json | 2 +- .vscode/tasks.json | 1 + Dockerfile | 2 +- .../_examples/visualise.ipynb | 21 +- docs/cli.rst | 8 +- docs/conf.py | 44 +- docs/examples.rst | 3 + docs/gen/platipy.cli.rst | 8 - docs/gen/platipy.dicom.io.rst | 46 + docs/gen/platipy.dicom.rst | 5 +- docs/gen/platipy.dicom.tests.rst | 12 +- docs/gen/platipy.imaging.generation.rst | 46 + docs/gen/platipy.imaging.label.rst | 36 +- docs/gen/platipy.imaging.projects.cardiac.rst | 8 - docs/gen/platipy.imaging.registration.rst | 22 +- docs/gen/platipy.imaging.rst | 4 +- docs/gen/platipy.imaging.tests.rst | 14 +- docs/gen/platipy.imaging.utils.rst | 28 +- docs/gen/platipy.imaging.visualisation.rst | 38 +- docs/getting_started.rst | 44 + docs/index.rst | 77 +- docs/registration.rst | 3 + docs/site/.buildinfo | 2 +- .../.doctrees/_examples/visualise.doctree | 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docs/site/_sources/getting_started.rst.txt create mode 100644 docs/site/_sources/registration.rst.txt create mode 100644 docs/site/_sources/visualiser.rst.txt create mode 100644 docs/site/_static/pygments_dark.css create mode 100644 docs/site/_static/scripts/main.js create mode 100644 docs/site/_static/scripts/main.js.map create mode 100644 docs/site/_static/styles/furo-extensions.css create mode 100644 docs/site/_static/styles/furo-extensions.css.map create mode 100644 docs/site/_static/styles/furo.css create mode 100644 docs/site/_static/styles/furo.css.map create mode 100644 docs/site/examples.html create mode 100644 docs/site/gen/platipy.dicom.io.html create mode 100644 docs/site/gen/platipy.imaging.generation.html create mode 100644 docs/site/getting_started.html create mode 100644 docs/site/registration.html create mode 100644 docs/site/visualiser.html create mode 100644 docs/visualiser.rst rename examples/{generate_synthetic_deformations => }/README.md (58%) create mode 100644 examples/animation.gif delete mode 100644 examples/animations/generate_animations.ipynb rename examples/{bronchus_segmentation => }/bronchus_segmentation.ipynb (100%) rename examples/{service/bronchus.ipynb => bronchus_service.ipynb} (100%) rename examples/{cardiac_atlas_segmentation => }/cardiac_segmentation.ipynb (100%) rename examples/{service/cardiac.ipynb => cardiac_service.ipynb} (100%) delete mode 100644 examples/download/README.md create mode 100644 examples/generate_animations.ipynb delete mode 100644 examples/generate_synthetic_deformations/generate_animations.ipynb rename examples/{generate_synthetic_deformations => }/generate_synthetic_head_neck_deformation.ipynb (90%) rename examples/{generate_synthetic_deformations/generate_synthetic_deformations.ipynb => generate_synthetic_lung_deformations.ipynb} (100%) delete mode 100644 examples/sample/1_convert_data.ipynb delete mode 100644 examples/sample/2_inter_observer_variability.ipynb delete mode 100644 examples/sample/3_registration.ipynb rename examples/{download => }/tcia.ipynb (100%) create mode 100644 examples/visualise.ipynb create mode 100644 platipy/dicom/tests/test_crawl.py diff --git a/.vscode/settings.json b/.vscode/settings.json index 7a28e90c..ca789f2d 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -28,5 +28,5 @@ "python.testing.unittestEnabled": false, "python.testing.nosetestsEnabled": false, "python.testing.pytestEnabled": true, - "python.pythonPath": "/home/robbie/.pyenv/versions/3.8.8/envs/platipy/bin/python" + "python.pythonPath": "/usr/bin/python" } \ No newline at end of file diff --git a/.vscode/tasks.json b/.vscode/tasks.json index d64b5a81..5c753ea8 100644 --- a/.vscode/tasks.json +++ b/.vscode/tasks.json @@ -94,6 +94,7 @@ "args": [ "-b", "html", + "-a", "docs", "docs/site" ], diff --git a/Dockerfile b/Dockerfile index 76bf654f..6c751fa4 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,6 +1,6 @@ FROM ubuntu:20.04 -RUN apt-get update; DEBIAN_FRONTEND="noninteractive" apt-get install -y redis-server git python3-pip libgl1-mesa-glx libsm6 libxext6 libxrender-dev libglib2.0-0 +RUN apt-get update; DEBIAN_FRONTEND="noninteractive" apt-get install -y redis-server git python3-pip libgl1-mesa-glx libsm6 libxext6 libxrender-dev libglib2.0-0 pandoc RUN ln -s /usr/bin/python3 /usr/bin/python RUN ln -s /usr/bin/pip3 /usr/bin/pip diff --git a/examples/visualise/visualise_segmentation.ipynb b/docs/_examples/visualise.ipynb similarity index 97% rename from examples/visualise/visualise_segmentation.ipynb rename to docs/_examples/visualise.ipynb index a6128487..1bdd986e 100644 --- a/examples/visualise/visualise_segmentation.ipynb +++ b/docs/_examples/visualise.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Visualise Image Example\n", + "# Visualise Images\n", "\n", "This notebook demonstrates how to display an image along with some contours, scalar overlays and bounding boxes.\n", "\n", @@ -19,10 +19,6 @@ "metadata": {}, "outputs": [], "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "%matplotlib inline\n", - "\n", "# Check if platipy is installed, if not install it.\n", "try:\n", " import platipy\n", @@ -45,7 +41,7 @@ "# Some utilities we use for generating interesting data\n", "from platipy.imaging.registration.utils import convert_mask_to_reg_structure, apply_transform\n", "from platipy.imaging.registration.deformable import fast_symmetric_forces_demons_registration\n", - "from platipy.imaging.generation.dvf import generate_field_expand\n", + "from platipy.imaging.generation.dvf import generate_field_asymmetric_extend\n", "from platipy.imaging.label.utils import get_com\n", "from platipy.imaging.utils.crop import label_to_roi" ] @@ -213,6 +209,13 @@ "fig = image_visualiser.show()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -228,7 +231,7 @@ "metadata": {}, "outputs": [], "source": [ - "contours[\"HEART_EXPAND\"], tfm_expansion, dvf_expansion = generate_field_expand(contours[\"HEART\"], expand=20)" + "contours[\"HEART_EXPAND\"], tfm_expansion, dvf_expansion = generate_field_asymmetric_extend(contours[\"HEART\"])" ] }, { @@ -368,9 +371,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "2.7.16" + "version": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file diff --git a/docs/cli.rst b/docs/cli.rst index 6a53fb2d..b28c23f8 100644 --- a/docs/cli.rst +++ b/docs/cli.rst @@ -1,10 +1,6 @@ - +##################### Command Line Tools -================== - -.. contents:: - :local: - :backlinks: entry +##################### .. click:: platipy.cli.dicom_crawler:click_command :prog: dicom_crawler diff --git a/docs/conf.py b/docs/conf.py index 41c13be8..d02ebba5 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -12,15 +12,17 @@ # import os import sys -sys.path.insert(0, os.path.abspath('platipy')) +import shutil -html_theme = 'alabaster' +sys.path.insert(0, os.path.abspath("platipy")) + +html_theme = "furo" # -- Project information ----------------------------------------------------- -project = 'PlatiPy' -copyright = '2020, Phillip Chlap & Rob Finnegan' -author = 'Phillip Chlap & Rob Finnegan' +project = "PlatiPy" +copyright = "2021, Phillip Chlap & Rob Finnegan" +author = "Phillip Chlap & Rob Finnegan" # -- General configuration --------------------------------------------------- @@ -28,22 +30,40 @@ # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. -extensions = ['sphinx.ext.autodoc', 'sphinx.ext.coverage', 'sphinx.ext.napoleon', 'sphinx_click'] +extensions = [ + "sphinx.ext.autodoc", + "sphinx.ext.coverage", + "sphinx.ext.napoleon", + "sphinx_click", + "nbsphinx", +] # Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] +templates_path = ["_templates"] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', 'site'] +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "site"] # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] +html_static_path = ["_static"] html_show_sphinx = False -html_theme = 'neo_rtd_theme' -import sphinx_theme -html_theme_path = [sphinx_theme.get_html_theme_path()] + +print("Copy example notebooks into docs/_examples") + + +def all_but_ipynb(directory, contents): + result = [] + for c in contents: + if os.path.isfile(os.path.join(directory, c)) and (not c.endswith(".ipynb")): + result += [c] + return result + + +shutil.rmtree("_examples", ignore_errors=True) +os.mkdir("_examples") +shutil.copy("../examples/visualise.ipynb", "_examples/visualise.ipynb") diff --git a/docs/examples.rst b/docs/examples.rst new file mode 100644 index 00000000..84ecd428 --- /dev/null +++ b/docs/examples.rst @@ -0,0 +1,3 @@ +##################### +Examples +##################### diff --git a/docs/gen/platipy.cli.rst b/docs/gen/platipy.cli.rst index a3d032ba..0acb2d02 100644 --- a/docs/gen/platipy.cli.rst +++ b/docs/gen/platipy.cli.rst @@ -52,14 +52,6 @@ platipy.cli.segmentation module :undoc-members: :show-inheritance: -platipy.cli.sort\_dicom\_from\_directory module ------------------------------------------------ - -.. automodule:: platipy.cli.sort_dicom_from_directory - :members: - :undoc-members: - :show-inheritance: - platipy.cli.tcia\_download module --------------------------------- diff --git a/docs/gen/platipy.dicom.io.rst b/docs/gen/platipy.dicom.io.rst new file mode 100644 index 00000000..20566e98 --- /dev/null +++ b/docs/gen/platipy.dicom.io.rst @@ -0,0 +1,46 @@ +platipy.dicom.io package +======================== + +Submodules +---------- + +platipy.dicom.io.crawl module +----------------------------- + +.. automodule:: platipy.dicom.io.crawl + :members: + :undoc-members: + :show-inheritance: + +platipy.dicom.io.nifti\_to\_rtstruct module +------------------------------------------- + +.. automodule:: platipy.dicom.io.nifti_to_rtstruct + :members: + :undoc-members: + :show-inheritance: + +platipy.dicom.io.nifti\_to\_series module +----------------------------------------- + +.. automodule:: platipy.dicom.io.nifti_to_series + :members: + :undoc-members: + :show-inheritance: + +platipy.dicom.io.rtstruct\_to\_nifti module +------------------------------------------- + +.. automodule:: platipy.dicom.io.rtstruct_to_nifti + :members: + :undoc-members: + :show-inheritance: + + +Module contents +--------------- + +.. automodule:: platipy.dicom.io + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/gen/platipy.dicom.rst b/docs/gen/platipy.dicom.rst index 2165fd13..2023e293 100644 --- a/docs/gen/platipy.dicom.rst +++ b/docs/gen/platipy.dicom.rst @@ -8,11 +8,8 @@ Subpackages :maxdepth: 4 platipy.dicom.communication - platipy.dicom.dicom_directory_crawler platipy.dicom.download - platipy.dicom.nifti_to_rtstruct - platipy.dicom.nifti_to_series - platipy.dicom.rtstruct_to_nifti + platipy.dicom.io platipy.dicom.tests Module contents diff --git a/docs/gen/platipy.dicom.tests.rst b/docs/gen/platipy.dicom.tests.rst index 639aa9a0..4580dc97 100644 --- a/docs/gen/platipy.dicom.tests.rst +++ b/docs/gen/platipy.dicom.tests.rst @@ -4,18 +4,18 @@ platipy.dicom.tests package Submodules ---------- -platipy.dicom.tests.test\_convert\_nifti module ------------------------------------------------ +platipy.dicom.tests.test\_convert module +---------------------------------------- -.. automodule:: platipy.dicom.tests.test_convert_nifti +.. automodule:: platipy.dicom.tests.test_convert :members: :undoc-members: :show-inheritance: -platipy.dicom.tests.test\_convert\_rtstruct module --------------------------------------------------- +platipy.dicom.tests.test\_crawl module +-------------------------------------- -.. automodule:: platipy.dicom.tests.test_convert_rtstruct +.. automodule:: platipy.dicom.tests.test_crawl :members: :undoc-members: :show-inheritance: diff --git a/docs/gen/platipy.imaging.generation.rst b/docs/gen/platipy.imaging.generation.rst new file mode 100644 index 00000000..0670e19e --- /dev/null +++ b/docs/gen/platipy.imaging.generation.rst @@ -0,0 +1,46 @@ +platipy.imaging.generation package +================================== + +Submodules +---------- + +platipy.imaging.generation.augment module +----------------------------------------- + +.. automodule:: platipy.imaging.generation.augment + :members: + :undoc-members: + :show-inheritance: + +platipy.imaging.generation.dvf module +------------------------------------- + +.. automodule:: platipy.imaging.generation.dvf + :members: + :undoc-members: + :show-inheritance: + +platipy.imaging.generation.image module +--------------------------------------- + +.. automodule:: platipy.imaging.generation.image + :members: + :undoc-members: + :show-inheritance: + +platipy.imaging.generation.mask module +-------------------------------------- + +.. automodule:: platipy.imaging.generation.mask + :members: + :undoc-members: + :show-inheritance: + + +Module contents +--------------- + +.. automodule:: platipy.imaging.generation + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/gen/platipy.imaging.label.rst b/docs/gen/platipy.imaging.label.rst index 229a5c81..3cce268d 100644 --- a/docs/gen/platipy.imaging.label.rst +++ b/docs/gen/platipy.imaging.label.rst @@ -4,18 +4,42 @@ platipy.imaging.label package Submodules ---------- -platipy.imaging.label.label\_comparison module ----------------------------------------------- +platipy.imaging.label.comparison module +--------------------------------------- -.. automodule:: platipy.imaging.label.label_comparison +.. automodule:: platipy.imaging.label.comparison :members: :undoc-members: :show-inheritance: -platipy.imaging.label.label\_operations module ----------------------------------------------- +platipy.imaging.label.fusion module +----------------------------------- -.. automodule:: platipy.imaging.label.label_operations +.. automodule:: platipy.imaging.label.fusion + :members: + :undoc-members: + :show-inheritance: + +platipy.imaging.label.iar module +-------------------------------- + +.. automodule:: platipy.imaging.label.iar + :members: + :undoc-members: + :show-inheritance: + +platipy.imaging.label.projection module +--------------------------------------- + +.. automodule:: platipy.imaging.label.projection + :members: + :undoc-members: + :show-inheritance: + +platipy.imaging.label.utils module +---------------------------------- + +.. automodule:: platipy.imaging.label.utils :members: :undoc-members: :show-inheritance: diff --git a/docs/gen/platipy.imaging.projects.cardiac.rst b/docs/gen/platipy.imaging.projects.cardiac.rst index c848b01a..79deac08 100644 --- a/docs/gen/platipy.imaging.projects.cardiac.rst +++ b/docs/gen/platipy.imaging.projects.cardiac.rst @@ -20,14 +20,6 @@ platipy.imaging.projects.cardiac.service module :undoc-members: :show-inheritance: -platipy.imaging.projects.cardiac.utils module ---------------------------------------------- - -.. automodule:: platipy.imaging.projects.cardiac.utils - :members: - :undoc-members: - :show-inheritance: - Module contents --------------- diff --git a/docs/gen/platipy.imaging.registration.rst b/docs/gen/platipy.imaging.registration.rst index e8dd8636..aa12582a 100644 --- a/docs/gen/platipy.imaging.registration.rst +++ b/docs/gen/platipy.imaging.registration.rst @@ -4,10 +4,26 @@ platipy.imaging.registration package Submodules ---------- -platipy.imaging.registration.registration module ------------------------------------------------- +platipy.imaging.registration.deformable module +---------------------------------------------- -.. automodule:: platipy.imaging.registration.registration +.. automodule:: platipy.imaging.registration.deformable + :members: + :undoc-members: + :show-inheritance: + +platipy.imaging.registration.linear module +------------------------------------------ + +.. automodule:: platipy.imaging.registration.linear + :members: + :undoc-members: + :show-inheritance: + +platipy.imaging.registration.utils module +----------------------------------------- + +.. automodule:: platipy.imaging.registration.utils :members: :undoc-members: :show-inheritance: diff --git a/docs/gen/platipy.imaging.rst b/docs/gen/platipy.imaging.rst index 643afe89..03c08b7c 100644 --- a/docs/gen/platipy.imaging.rst +++ b/docs/gen/platipy.imaging.rst @@ -7,9 +7,7 @@ Subpackages .. toctree:: :maxdepth: 4 - platipy.imaging.atlas - platipy.imaging.augment - platipy.imaging.deformation_fields + platipy.imaging.generation platipy.imaging.label platipy.imaging.projects platipy.imaging.registration diff --git a/docs/gen/platipy.imaging.tests.rst b/docs/gen/platipy.imaging.tests.rst index cd5cadae..738c7d60 100644 --- a/docs/gen/platipy.imaging.tests.rst +++ b/docs/gen/platipy.imaging.tests.rst @@ -4,10 +4,10 @@ platipy.imaging.tests package Submodules ---------- -platipy.imaging.tests.pull\_data module ---------------------------------------- +platipy.imaging.tests.data module +--------------------------------- -.. automodule:: platipy.imaging.tests.pull_data +.. automodule:: platipy.imaging.tests.data :members: :undoc-members: :show-inheritance: @@ -28,6 +28,14 @@ platipy.imaging.tests.test\_cardiac module :undoc-members: :show-inheritance: +platipy.imaging.tests.test\_visualiser module +--------------------------------------------- + +.. automodule:: platipy.imaging.tests.test_visualiser + :members: + :undoc-members: + :show-inheritance: + Module contents --------------- diff --git a/docs/gen/platipy.imaging.utils.rst b/docs/gen/platipy.imaging.utils.rst index e6d7aad0..fda4ea1a 100644 --- a/docs/gen/platipy.imaging.utils.rst +++ b/docs/gen/platipy.imaging.utils.rst @@ -4,10 +4,34 @@ platipy.imaging.utils package Submodules ---------- -platipy.imaging.utils.tools module +platipy.imaging.utils.crop module +--------------------------------- + +.. automodule:: platipy.imaging.utils.crop + :members: + :undoc-members: + :show-inheritance: + +platipy.imaging.utils.lung module +--------------------------------- + +.. automodule:: platipy.imaging.utils.lung + :members: + :undoc-members: + :show-inheritance: + +platipy.imaging.utils.valve module ---------------------------------- -.. automodule:: platipy.imaging.utils.tools +.. automodule:: platipy.imaging.utils.valve + :members: + :undoc-members: + :show-inheritance: + +platipy.imaging.utils.vessel module +----------------------------------- + +.. automodule:: platipy.imaging.utils.vessel :members: :undoc-members: :show-inheritance: diff --git a/docs/gen/platipy.imaging.visualisation.rst b/docs/gen/platipy.imaging.visualisation.rst index f1834a6b..eaf0c589 100644 --- a/docs/gen/platipy.imaging.visualisation.rst +++ b/docs/gen/platipy.imaging.visualisation.rst @@ -4,50 +4,26 @@ platipy.imaging.visualisation package Submodules ---------- -platipy.imaging.visualisation.Python3DMesh module -------------------------------------------------- +platipy.imaging.visualisation.animation module +---------------------------------------------- -.. automodule:: platipy.imaging.visualisation.Python3DMesh +.. automodule:: platipy.imaging.visualisation.animation :members: :undoc-members: :show-inheritance: -platipy.imaging.visualisation.VesselSurfaces\-Auto module ---------------------------------------------------------- - -.. automodule:: platipy.imaging.visualisation.VesselSurfaces-Auto - :members: - :undoc-members: - :show-inheritance: - -platipy.imaging.visualisation.crosshairs module ------------------------------------------------ - -.. automodule:: platipy.imaging.visualisation.crosshairs - :members: - :undoc-members: - :show-inheritance: - -platipy.imaging.visualisation.surface\_operations module --------------------------------------------------------- - -.. automodule:: platipy.imaging.visualisation.surface_operations - :members: - :undoc-members: - :show-inheritance: - -platipy.imaging.visualisation.tools module +platipy.imaging.visualisation.utils module ------------------------------------------ -.. automodule:: platipy.imaging.visualisation.tools +.. automodule:: platipy.imaging.visualisation.utils :members: :undoc-members: :show-inheritance: -platipy.imaging.visualisation.vis\_tools module +platipy.imaging.visualisation.visualiser module ----------------------------------------------- -.. automodule:: platipy.imaging.visualisation.vis_tools +.. automodule:: platipy.imaging.visualisation.visualiser :members: :undoc-members: :show-inheritance: diff --git a/docs/getting_started.rst b/docs/getting_started.rst new file mode 100644 index 00000000..e1e0540c --- /dev/null +++ b/docs/getting_started.rst @@ -0,0 +1,44 @@ +##################### +Getting Started +##################### + +************ +Installation +************ + +Currently this repository is not available in the official Python repositories. However, you can +install it with pip using the following command: + +``pip install git+git://github.com/pyplati/platipy`` + +If you have already installed the library, you can install the latest updates using: + +``pip install --upgrade git+git://github.com/pyplati/platipy`` + +*************** +Getting Started +*************** + +Command Line Interface (CLI) +============================ + +Once you have installed PlatiPy, the ``platipy`` command should be available in your environment. +Try the following command to check that is is working: + +``platipy --help`` + +This lists the different tools which are available via the command line interface. Learn more about +these tools and how to use them here: :doc:`here `. + +Python +====== + +Jupyter Notebooks +----------------- + +The following Jupyter Notebooks are provided to demonstrate how to use some of the PlatiPy +functionality from your Python code: + +* `Nifti to RTStruct `_ +* `RTStruct to Nifti `_ +* `Visualise Segmentation `_ diff --git a/docs/index.rst b/docs/index.rst index 83258c8d..4c527f06 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,19 +1,7 @@ -.. PlatiPy documentation master file, created by - sphinx-quickstart on Thu Nov 19 00:32:10 2020. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - ##################### PlatiPy documentation ##################### - -.. toctree:: - :maxdepth: 3 - :caption: Contents: - - index - PlatiPy is a **P**\ rocessing **L**\ ibrary and **A**\ nalysis **T**\ oolkit for Medical **I**\ maging in **Py**\ thon. It contains a collections of tools and scripts useful for processing and analysis of medical images. @@ -22,56 +10,25 @@ Generally these tools are accesible via a Command Line Interface or by calling t script directly. Examples are provided in Jupyter Notebooks to demonstrate how to use them from within Python code. -************ -Installation -************ - -Currently this repository is not available in the official Python repositories. However, you can -install it with pip using the following command: - -``pip install git+git://github.com/pyplati/platipy`` - -If you have already installed the library, you can install the latest updates using: - -``pip install --upgrade git+git://github.com/pyplati/platipy`` - -*************** -Getting Started -*************** - -Command Line Interface (CLI) -============================ - -Once you have installed PlatiPy, the ``platipy`` command should be available in your environment. -Try the following command to check that is is working: - -``platipy --help`` - -This lists the different tools which are available via the command line interface. Learn more about -these tools and how to use them here: :doc:`here `. - -Python -====== - -Jupyter Notebooks ------------------ - -The following Jupyter Notebooks are provided to demonstrate how to use some of the PlatiPy -functionality from your Python code: +.. toctree:: + :caption: Introduction + :maxdepth: 2 + :hidden: -* `Nifti to RTStruct `_ -* `RTStruct to Nifti `_ -* `Visualise Segmentation `_ + getting_started -Module Reference ----------------- +.. toctree:: + :caption: Features + :maxdepth: 2 + :hidden: -You can find more detailed documentation on how to use the different classes and functions found in -PlatiPy :doc:`here `. + visualiser + registration + cli -***** -Links -***** +.. toctree:: + :caption: Examples + :maxdepth: 2 + :hidden: -* :doc:`cli` -* :doc:`PlatiPy Modules ` + _examples/visualise diff --git a/docs/registration.rst b/docs/registration.rst new file mode 100644 index 00000000..2ecc770d --- /dev/null +++ b/docs/registration.rst @@ -0,0 +1,3 @@ +##################### +Registration +##################### diff --git a/docs/site/.buildinfo b/docs/site/.buildinfo index b0ffe85d..a1994dc0 100644 --- a/docs/site/.buildinfo +++ b/docs/site/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. 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-
--structure_format <structure_format>
@@ -228,7 +205,6 @@

dicom_crawler

-
--dose_format <dose_format>
@@ -239,7 +215,6 @@

dicom_crawler

-
--overwrite
@@ -250,7 +225,6 @@

dicom_crawler

-
--file_suffix <file_suffix>
@@ -261,10 +235,9 @@

dicom_crawler

-
--s, --short_description
+-s, --short_description

Use less verbose descriptions for DICOM images.

Default
@@ -272,10 +245,19 @@

dicom_crawler

- - -
-

segmentation

+
+
+-v, --verbose
+

Print more information while running.

+
+
Default
+

False

+
+
+
+ +
+

segmentation

Run an auto-segmentation on an input image. Choose which algorithm to run and pass the path to the Nitfti input image OR a directory containing a DICOM series.

Optionally, pass in a configuration file for the segmentation algorithm. Output the default @@ -286,38 +268,33 @@

segmentationOptions

--c, --config <config>
+-c, --config <config>

Path to JSON file containing algorithm settings

-
--d, --default
+-d, --default

Print the default configuration for the selected algorithm

-
--o, --output <output>
+-o, --output <output>

Path to directory for output

-

Arguments

ALGORITHM

Required argument

-
INPUT_PATH

Optional argument

- -

-
-

nifti_to_rtstruct

+ +
+

nifti_to_rtstruct

Convert Nifti masks to Dicom RTStruct

nifti_to_rtstruct [OPTIONS]
 
@@ -325,31 +302,22 @@

nifti_to_rtstructOptions

--d, --dcm_file <dcm_file>
+-d, --dcm_file <dcm_file>

Required Reference DICOM file from which header tags will be copied

- -
-
---debug
-

Whether intermediate debug info is written

-
-
--m, --mask <mask>
+-m, --mask <mask>

Required Mask pairs with name,filename

-
--o, --out_rt_filename <out_rt_filename>
+-o, --out_rt_filename <out_rt_filename>

Required Name of RT struct output

- -

-
+
+

nifti_to_series

Convert a Nifti image to a Dicom image series

nifti_to_series [OPTIONS]
 
@@ -357,25 +325,22 @@

nifti_to_seriesOptions

--d, --dcm <dcm>
+-d, --dcm <dcm>

Required Reference DICOM folder containing series from which header tags will be copied

-
--i, --image <image>
+-i, --image <image>

Required Nifti Image file to convert to Dicom series

-
--t, --tag <tag>
+-t, --tag <tag>

Override Dicom Tags by providing key:value pairs

-
--o, --output_directory <output_directory>
+-o, --output_directory <output_directory>

Directory in which Dicom series files will be generated

Default
@@ -383,10 +348,9 @@

nifti_to_series

- -

-
+
+

rtstruct_to_nifti

Convert a DICOM RTSTRUCT file to Nifti format.

rtstruct_to_nifti [OPTIONS]
 
@@ -394,25 +358,22 @@

rtstruct_to_niftiOptions

--i, --dcm_img <dcm_img>
+-i, --dcm_img <dcm_img>

Required Directory containing the image series linked to the contour to convert

-
--r, --dcm_rt_file <dcm_rt_file>
+-r, --dcm_rt_file <dcm_rt_file>

Required Dicom RTStruct file containing the contours to convert

-
--p, --prefix <prefix>
-

Prefix for output files (e.g. Case_01_

+-p, --prefix <prefix> +

Prefix for output files (e.g. Case_01_

-
--od, --output_dir <output_dir>
+-od, --output_dir <output_dir>

Directory in which to place converted files

Default
@@ -420,80 +381,93 @@

rtstruct_to_nifti
--oi, --output_img <output_img>
+-oi, --output_img <output_img>

Output name of converted image series

-
--spacing <spacing>

DICOM image spacing override with format x,y,z (0 indicates to leave as is, e.g. 0,0,3)

+

+ -
- + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/examples.html b/docs/site/examples.html new file mode 100644 index 00000000..ddae1999 --- /dev/null +++ b/docs/site/examples.html @@ -0,0 +1,177 @@ + + + + + + + + + Examples - PlatiPy documentation + + + + + + + + + + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
+
+
+ +
+ +
+ +
+
+ +
+
+
+ +
+

Examples

+
+ +
+
+ + + + + +
+
+ +
+
+ + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/modules.html b/docs/site/gen/modules.html index d2e27f8b..48b8d9fd 100644 --- a/docs/site/gen/modules.html +++ b/docs/site/gen/modules.html @@ -1,151 +1,141 @@ + + + + + + + + + platipy - PlatiPy documentation + + + + - - - - - - - - - platipy — PlatiPy documentation - - - + + - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
+
+
+ +
+ +
+ +
+
+ +
+ - - -
- - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.backend.html b/docs/site/gen/platipy.backend.html index 1e6edbf2..bfb1d90f 100644 --- a/docs/site/gen/platipy.backend.html +++ b/docs/site/gen/platipy.backend.html @@ -1,169 +1,159 @@ + + + + + + + + + platipy.backend package - PlatiPy documentation + + + + - - - - - - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
+
+
+ +
+ +
+ +
+
+ +
+ -
+ +

platipy.backend.api module

@@ -173,24 +163,19 @@

Submodules endpoint = 'algorithmendpoint'

-
get()
-
mediatypes()
-
methods = {'GET'}
- -
class platipy.backend.api.CustomConfig
@@ -199,9 +184,7 @@

Submodules RESTFUL_JSON = {'cls': <class 'platipy.backend.models.AlchemyEncoder'>, 'indent': 2, 'separators': (', ', ': ')}

- -
class platipy.backend.api.DataObjectDownloadEndpoint
@@ -210,30 +193,24 @@

Submodules endpoint = 'dataobjectdownloadendpoint'

-
get(dataobject_id)

Returns the file for the given dataobject

-
mediatypes()
-
methods = {'GET'}
-
parser = <flask_restful.reqparse.RequestParser object>
- -
class platipy.backend.api.DataObjectEndpoint
@@ -242,39 +219,31 @@

Submodules delete(dataobject_id)

-
endpoint = 'dataobjectendpoint'
-
get(dataobject_id)
-
mediatypes()
-
methods = {'DELETE', 'GET', 'POST'}
-
parser = <flask_restful.reqparse.RequestParser object>
-
post()
- -
class platipy.backend.api.DataObjectsEndpoint
@@ -283,24 +252,19 @@

Submodules endpoint = 'dataobjectsendpoint'

-
get()
-
mediatypes()
-
methods = {'GET'}
- -
class platipy.backend.api.DatasetEndpoint
@@ -309,34 +273,27 @@

Submodules endpoint = 'datasetendpoint'

-
get(dataset_id)
-
mediatypes()
-
methods = {'GET', 'POST'}
-
parser = <flask_restful.reqparse.RequestParser object>
-
post()
- -
class platipy.backend.api.DatasetReadyEndpoint
@@ -345,29 +302,23 @@

Submodules endpoint = 'datasetreadyendpoint'

-
get(dataset_id)
-
mediatypes()
-
methods = {'GET'}
-
parser = <flask_restful.reqparse.RequestParser object>
- -
class platipy.backend.api.DatasetsEndpoint
@@ -376,24 +327,19 @@

Submodules endpoint = 'datasetsendpoint'

-
get()
-
mediatypes()
-
methods = {'GET'}
- -
class platipy.backend.api.DicomLocationEndpoint
@@ -402,34 +348,27 @@

Submodules endpoint = 'dicomlocationendpoint'

-
get()
-
mediatypes()
-
methods = {'GET', 'POST'}
-
parser = <flask_restful.reqparse.RequestParser object>
-
post()
- -
class platipy.backend.api.Resource
@@ -438,9 +377,7 @@

Submodules method_decorators = [<function authenticate>]

- -
class platipy.backend.api.TaskStatus
@@ -449,25 +386,20 @@

Submodules endpoint = 'taskstatus'

-
get(task_id)

Get the status of a task given the ID

-
mediatypes()
-
methods = {'GET'}
- -
class platipy.backend.api.TriggerEndpoint
@@ -476,36 +408,29 @@

Submodules endpoint = 'triggerendpoint'

-
mediatypes()
-
methods = {'POST'}
-
parser = <flask_restful.reqparse.RequestParser object>
-
post()
- -
platipy.backend.api.authenticate(func)
- -
-
+ +

platipy.backend.application module

@@ -515,9 +440,7 @@

Submodules settings_to_json()

- -
class platipy.backend.application.FlaskApp(import_name, static_url_path=None, static_folder='static', static_host=None, host_matching=False, subdomain_matching=False, template_folder='templates', instance_path=None, instance_relative_config=False, root_path=None)
@@ -527,37 +450,30 @@

Submodules algorithms = {}

-
api = None
-
beat_started = False
-
celery_started = False
-
dicom_listener_aetitle = 'PLATIPY_SERVICE'
-
dicom_listener_port = 7777
-
register(name, default_settings=None)
-
run(host=None, port=None, debug=None, dicom_listener_port=7777, dicom_listener_aetitle='PLATIPY_SERVICE', load_dotenv=True, **options)
@@ -617,27 +533,22 @@

Submodules
run_beat()

-
run_celery()
-
run_dicom_listener(listen_port, listen_ae_title)

Background task that listens at a specific port for incoming dicom series

- - -
-
+ +

platipy.backend.client module

@@ -670,7 +581,6 @@

Submodules
add_dataset(from_dicom_location=None, to_dicom_location=None, timeout=None)
@@ -692,7 +602,6 @@

Submodules
add_dicom_location(name, host, port, ae_title=None)
@@ -713,7 +622,6 @@

Submodules
download_output_objects(dataset, output_path='.')
@@ -727,7 +635,6 @@

Submodules
get_dataset(dataset)
@@ -741,7 +648,6 @@

Submodules
get_dataset_ready(dataset)
@@ -755,7 +661,6 @@

Submodules
get_default_settings()
@@ -766,7 +671,6 @@

Submodules
get_dicom_location(name)
@@ -780,7 +684,6 @@

Submodules
run_algorithm(dataset, config=None)
@@ -794,11 +697,9 @@

Submodules +

+

platipy.backend.manage module

@@ -806,37 +707,31 @@

Submodules
platipy.backend.manage.create_db()

Create the Database used by the framework

- -
-
+ +

platipy.backend.models module

class platipy.backend.models.APIKey(**kwargs)
-

Bases: sqlalchemy.ext.declarative.api.Model

+

Bases: sqlalchemy.orm.decl_api.Model

is_admin
-
key
-
name
-
-
class platipy.backend.models.AlchemyEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)
@@ -861,283 +756,244 @@

Submodules
class platipy.backend.models.DataObject(**kwargs)
-

Bases: sqlalchemy.ext.declarative.api.Model

+

Bases: sqlalchemy.orm.decl_api.Model

children
-
dataset
-
dataset_id
-
id
-
is_fetched
-
is_input
-
is_sent
-
meta_data
-
parent
-
parent_id
-
path
-
series_instance_uid
-
status
-
timestamp
-
type
-

-
class platipy.backend.models.Dataset(**kwargs)
-

Bases: sqlalchemy.ext.declarative.api.Model

+

Bases: sqlalchemy.orm.decl_api.Model

from_dicom_location
-
from_dicom_location_id
-
id
-
input_data_objects
-
output_data_objects
-
owner_key
-
timeout
-
timestamp
-
to_dicom_location
-
to_dicom_location_id
-
-
class platipy.backend.models.DicomLocation(**kwargs)
-

Bases: sqlalchemy.ext.declarative.api.Model

+

Bases: sqlalchemy.orm.decl_api.Model

ae_title
-
host
-
id
-
name
-
owner_key
-
port
-
-
platipy.backend.models.default_timeout()
- -
-
+ +

platipy.backend.tasks module

-
-
+ +

platipy.backend.views module

platipy.backend.views.add_endpoint()
-
platipy.backend.views.dashboard()

Entry point to the dashboard of the application

-
platipy.backend.views.fetch_log()
-
platipy.backend.views.fetch_status()
-
platipy.backend.views.view_endpoint(id)
- -
-
+ +

Module contents

-
-
- - - - -
- - -
- -
-

- © Copyright 2020, Phillip Chlap & Rob Finnegan. + + -

-
+ +
+ + -
+ + +
+ + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.backend.sample.html b/docs/site/gen/platipy.backend.sample.html index 40f37093..a749b588 100644 --- a/docs/site/gen/platipy.backend.sample.html +++ b/docs/site/gen/platipy.backend.sample.html @@ -1,226 +1,207 @@ + + + + + + + + + platipy.backend.sample package - PlatiPy documentation + + + + - - - - - - - - - platipy.backend.sample package — PlatiPy documentation - - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
+
+
+ +
+ +
+ +
+
+ +
+
+
+ +

platipy.backend.sample package

-
+

Submodules

-
-
-

platipy.backend.sample.sample module

-
-
-platipy.backend.sample.sample.primitive_body_segmentation(data_objects, working_dir, settings)
-
- -
-
+
+
+

platipy.backend.sample.sample module

+
+

Module contents

-
-
+ + + +
+ + - - -
- - -
- -
-

- © Copyright 2020, Phillip Chlap & Rob Finnegan. - -

-
- -
+ + +
+ + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.cli.html b/docs/site/gen/platipy.cli.html index ac9a29a7..9649b555 100644 --- a/docs/site/gen/platipy.cli.html +++ b/docs/site/gen/platipy.cli.html @@ -1,247 +1,236 @@ + + + + + + + + + platipy.cli package - PlatiPy documentation + + + + - - - - - - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
+
+
+ +
+ +
+ +
+
+ +
+
+
+ +

platipy.cli package

-
+

Submodules

-
-
+
+

platipy.cli.dicom_crawler module

-
-
+ +

platipy.cli.nifti_to_rtstruct module

-
-
+ +

platipy.cli.nifti_to_series module

-
-
+ +

platipy.cli.rtstruct_to_nifti module

-
-
+ +

platipy.cli.run module

platipy.cli.run.platipy_cli()
-
- -
-
+

Run the PlatiPy Command Line Interface

+
+ +

platipy.cli.segmentation module

-
-
-

platipy.cli.sort_dicom_from_directory module

-
-
+ +

platipy.cli.tcia_download module

-
-
+ +

Module contents

-
-
- - - - -
- - -
+ + -
-

- © Copyright 2020, Phillip Chlap & Rob Finnegan. - -

-
+ +
+ + -
+ + +
+ + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.dicom.communication.html b/docs/site/gen/platipy.dicom.communication.html index 7dd4b7dd..c819044e 100644 --- a/docs/site/gen/platipy.dicom.communication.html +++ b/docs/site/gen/platipy.dicom.communication.html @@ -1,201 +1,183 @@ + + + + + + + + + platipy.dicom.communication package - PlatiPy documentation + + + + - - - - - - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
+
+
+ +
+ +
+ +
+
+ +
+
+
+ +

platipy.dicom.communication package

-
+

Submodules

-
-
+
+

platipy.dicom.communication.connector module

-class platipy.dicom.communication.connector.DicomConnector(host='127.0.0.1', port=0, ae_title='', output_directory='/tmp/tmpfe58ipc7')
+class platipy.dicom.communication.connector.DicomConnector(host='127.0.0.1', port=0, ae_title='', output_directory='/tmp/tmp_xcfu129')

Bases: object

do_find(dataset, query_model='1.2.840.10008.5.1.4.1.2.1.1')
-
download_series(series_instance_uid, recieved_callback=None, query_model='1.2.840.10008.5.1.4.1.2.1.3')
-
get_series_for_study(study_instance_uid, modality)
-
get_studies_for_patient(patient_id)
-
listen(recieved_callback, ae_title='PYNETDICOM')
-
move_series(seriesInstanceUID, move_aet='PYNETDICOM', query_model='1.2.840.10008.5.1.4.1.2.1.2')
-
on_association_accepted(event)
-
on_association_released(event)
-
on_c_echo(event)
@@ -218,90 +200,81 @@

Submodules
on_c_store(event)

-
send_dcm(dcm_file)

send_dcm accepts the location of a single dcm file to send over the connector or a list of dcm files to send in one association.

-
verify()
-
- -
-
+ +

Module contents

-
-
- - - - -
- + + -
- -
-

- © Copyright 2020, Phillip Chlap & Rob Finnegan. - -

-
+ +
+ + -
+ + +
+ + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.dicom.dicom_directory_crawler.html b/docs/site/gen/platipy.dicom.dicom_directory_crawler.html index 3cbdc1af..c28a568e 100644 --- a/docs/site/gen/platipy.dicom.dicom_directory_crawler.html +++ b/docs/site/gen/platipy.dicom.dicom_directory_crawler.html @@ -1,301 +1,207 @@ + + + + + + + + + platipy.dicom.dicom_directory_crawler package - PlatiPy documentation + + + + - - - - - - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
+
+
+ +
+ +
+ +
+
+ +
+
+
+ +
+

platipy.dicom.dicom_directory_crawler package

+
+

Submodules

+
+
+

platipy.dicom.dicom_directory_crawler.conversion_utils module

+
+
+

Module contents

+
+
-
+
-
  • platipy.dicom.dicom_directory_crawler package
  • - - -
  • - - - View page source +
  • - - - - -
    -
    -
    -
    - -
    -

    platipy.dicom.dicom_directory_crawler package

    -
    -

    Submodules

    -
    -
    -

    platipy.dicom.dicom_directory_crawler.conversion_utils module

    -
    -
    -platipy.dicom.dicom_directory_crawler.conversion_utils.fix_missing_data(contour_data_list)
    -

    Fixes missing points in contouring using simple linear interpolation

    -
    -
    Parameters
    -

    contour_data_list (list) – The contour data for each slice

    -
    -
    Returns
    -

    Interpolated contour data

    -
    -
    Return type
    -

    contour_data (numpy array)

    -
    -
    -
    + +
    -
    -
    -platipy.dicom.dicom_directory_crawler.conversion_utils.get_dicom_info_from_description(dicom_object, return_extra=False)
    -

    Attempts to return some information from a DICOM -This is typically used for naming converted NIFTI files

    -
    -
    Parameters
    -
      -
    • dicom_object (pydicom.dataset.FileDataset) – The DICOM object

    • -
    • return_extra (bool, optional) – return information that is usually not required

    • + + + +
    +
    -
    - - -
    - -
    -

    - © Copyright 2020, Phillip Chlap & Rob Finnegan. - -

    -
    - -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.dicom.download.html b/docs/site/gen/platipy.dicom.download.html index 14385d93..f9b963b9 100644 --- a/docs/site/gen/platipy.dicom.download.html +++ b/docs/site/gen/platipy.dicom.download.html @@ -1,156 +1,146 @@ + + + + + + + + + platipy.dicom.download package - PlatiPy documentation + + + + - - - - - - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
    +
    + +
    + +
    + +
    +
    + +
    +
    +
    + +

    platipy.dicom.download package

    -
    +

    Submodules

    -
    -
    +
    +

    platipy.dicom.download.tcia module

    @@ -177,7 +167,6 @@

    Submodules
    platipy.dicom.download.tcia.get_collections()
    @@ -191,7 +180,6 @@

    Submodules
    platipy.dicom.download.tcia.get_hn_data(number_of_patients=1)
    @@ -205,7 +193,6 @@

    Submodules
    platipy.dicom.download.tcia.get_lung_data(number_of_patients=1)
    @@ -219,7 +206,6 @@

    Submodules
    platipy.dicom.download.tcia.get_modalities_in_collection(collection)
    @@ -236,7 +222,6 @@

    Submodules
    platipy.dicom.download.tcia.get_patients_in_collection(collection)
    @@ -253,71 +238,66 @@

    Submodules +

    +

    Module contents

    -
    -
    - - - - -
    - - -
    - -
    -

    - © Copyright 2020, Phillip Chlap & Rob Finnegan. + + -

    -
    + +
    + + -
    + + +
    + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.dicom.html b/docs/site/gen/platipy.dicom.html index b95fb08e..a71807dc 100644 --- a/docs/site/gen/platipy.dicom.html +++ b/docs/site/gen/platipy.dicom.html @@ -1,153 +1,143 @@ + + + + + + + + + platipy.dicom package - PlatiPy documentation + + + + - - - - - - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
    +
    + +
    + +
    + +
    +
    + +
    + -
    - - - - -
    - - -
    + + -
    -

    - © Copyright 2020, Phillip Chlap & Rob Finnegan. - -

    -
    + +
    + + -
    + + +
    + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.dicom.io.html b/docs/site/gen/platipy.dicom.io.html new file mode 100644 index 00000000..b42a1638 --- /dev/null +++ b/docs/site/gen/platipy.dicom.io.html @@ -0,0 +1,437 @@ + + + + + + + + + platipy.dicom.io package - PlatiPy documentation + + + + + + + + + + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
    +
    + +
    + +
    + +
    +
    + +
    +
    +
    + +
    +

    platipy.dicom.io package

    +
    +

    Submodules

    +
    +
    +

    platipy.dicom.io.crawl module

    +
    +
    +platipy.dicom.io.crawl.fix_missing_data(contour_data_list)
    +

    Fixes missing points in contouring using simple linear interpolation

    +
    +
    Parameters
    +

    contour_data_list (list) – The contour data for each slice

    +
    +
    Returns
    +

    Interpolated contour data

    +
    +
    Return type
    +

    contour_data (numpy array)

    +
    +
    +
    +
    +
    +platipy.dicom.io.crawl.get_dicom_info_from_description(dicom_object, return_extra=False, sop_class_name='UNKNOWN')
    +

    Attempts to return some information from a DICOM +This is typically used for naming converted NIFTI files

    +
    +
    Parameters
    +
      +
    • dicom_object (pydicom.dataset.FileDataset) – The DICOM object

    • +
    • return_extra (bool, optional) – return information that is usually not required

    • +
    +
    +
    Returns
    +

    Some extracted information

    +
    +
    Return type
    +

    info (str)

    +
    +
    +
    +
    +
    +platipy.dicom.io.crawl.process_dicom_directory(dicom_directory, parent_sorting_field='PatientName', output_image_name_format='{parent_sorting_data}_{study_uid_index}_{Modality}_{image_desc}_{SeriesNumber}', output_structure_name_format='{parent_sorting_data}_{study_uid_index}_{Modality}_{structure_name}', output_dose_name_format='{parent_sorting_data}_{study_uid_index}_{DoseSummationType}', return_extra=True, output_directory='./', output_file_suffix='.nii.gz', overwrite_existing_files=False, write_to_disk=True, verbose=False, initial_sop_class_name_default='UNKNOWN')
    +
    +
    +
    +platipy.dicom.io.crawl.process_dicom_file_list(dicom_file_list, parent_sorting_field='PatientName', verbose=False)
    +

    Organise the DICOM files by the series UID

    +
    +
    +
    +platipy.dicom.io.crawl.process_dicom_series(dicom_series_dict, series_uid, parent_sorting_field='PatientName', return_extra=True, individual_file=False, initial_sop_class_name_default='UNKNOWN')
    +
    +
    +
    +platipy.dicom.io.crawl.safe_sort_dicom_image_list(dicom_image_list)
    +

    Sorts a list of DICOM image files based on a DICOM tag value. +This is a much safer method than reading SliceLocation. +It takes mandatory DICOM fields (Image Position [Patient]) and (Image Orientation [Patient]). +The list of DICOM files is sorted by projecting the image position onto the axis normal to the +place defined by the image orientation.

    +

    This accounts for differences in patient position (e.g. HFS/FFS).

    +
    +
    Parameters
    +

    dicom_image_list (list) – [description]

    +
    +
    +
    +
    +
    +platipy.dicom.io.crawl.transform_point_set_from_dicom_struct(image, dicom_struct, spacing_override=False)
    +

    This function is used to generate a binary mask from a set of vertices. +This allows us to convert from DICOM-RTStruct format to any imaging format.

    +
    +
    Parameters
    +
      +
    • image ([SimpleITK.Image]) – The image, used to copy imaging information +(e.g. resolution, spacing)

    • +
    • dicom_struct ([pydicom.Dataset]) – The DICOM-RTStruct file

    • +
    • spacing_override (bool | tuple, optional) – Overwrite the spacing. +Set with (axial_spacing, coronal_spacing, sagittal spacing). Defaults to False.

    • +
    +
    +
    Returns
    +

    final_struct_name_sequence, structure_list

    +
    +
    Return type
    +

    list, list

    +
    +
    +
    +
    +
    +platipy.dicom.io.crawl.write_output_data_to_disk(output_data_dict, output_directory='./', output_file_suffix='.nii.gz', overwrite_existing_files=False)
    +

    Write output to disk

    +
    +
    +
    +

    platipy.dicom.io.nifti_to_rtstruct module

    +
    +
    +platipy.dicom.io.nifti_to_rtstruct.convert_nifti(dcm_path, mask_input, output_file, color_map=<matplotlib.colors.LinearSegmentedColormap object>)
    +

    Convert a set of masks to a DICOM RTStruct object.

    +

    This function now utilises the rt-utils package: https://github.com/qurit/rt-utils +We keep this function in here for compatibility

    +
    +
    Parameters
    +
      +
    • dcm_path (str|pathlib.Path) – Path to the reference DICOM series

    • +
    • mask_input (dict|list) – A dictionary containing the name as key and image as value. Or a +list of string with comma separated name and mask paths (name,path)

    • +
    • output_file (str|pathlib.Path) –

      The path to the file to write the RTStruct +color_map (matplotlib.colors.Colormap, optional): Colormap to use for output. Defaults to

      +
      +

      cm.get_cmap(“rainbow”).

      +
      +

    • +
    +
    +
    +
    +
    +
    +

    platipy.dicom.io.nifti_to_series module

    +
    +
    +platipy.dicom.io.nifti_to_series.convert_nifti_to_dicom_series(image, reference_dcm, tag_overrides=[], output_directory='.')
    +

    Converts a Nifti image to a Dicom image series

    +
    +
    Parameters
    +
      +
    • image (sitk.Image) – A SimpleITK image object to convert

    • +
    • reference_dcm (str) – A directory path containing a reference Dicom series to use

    • +
    • tag_overrides (list, optional) – A list of tags to override containing tuples of +(key, value). Defaults to [].

    • +
    • output_directory (str, optional) – The directory in which to place the generated Dicom +files. Defaults to “.”.

    • +
    +
    +
    +
    +
    +
    +

    platipy.dicom.io.rtstruct_to_nifti module

    +
    +
    +platipy.dicom.io.rtstruct_to_nifti.convert_rtstruct(dcm_img, dcm_rt_file, prefix='Struct_', output_dir='.', output_img=None, spacing=None)
    +

    Convert a DICOM RTSTRUCT to NIFTI masks.

    +

    The masks are stored as NIFTI files in the output directory

    +
    +
    Parameters
    +
      +
    • dcm_img (str|pathlib.Path) – Path to the reference DICOM image series

    • +
    • dcm_rt_file (str|pathlib.Path) – Path to the DICOM RTSTRUCT file

    • +
    • prefix (str, optional) – The prefix to give the output files. Defaults to “Struct_”.

    • +
    • output_dir (str|pathlib.Path, optional) – Path to the output directory. Defaults to “.”.

    • +
    • output_img (str|pathlib.Path, optional) – If set, write the reference image to this file as +in NIFTI format. Defaults to None.

    • +
    • spacing (list, optional) – Values of image spacing to override. Defaults to None.

    • +
    +
    +
    +
    +
    +
    +platipy.dicom.io.rtstruct_to_nifti.fix_missing_data(contour_data)
    +

    Fixed a set of contour data if there are values missing

    +
    +
    Parameters
    +

    contour_data (pydicom.Sequence) – The contour sequence from the DICOM object

    +
    +
    Returns
    +

    The array of contour data with missing values fixed

    +
    +
    Return type
    +

    np.array

    +
    +
    +
    +
    +
    +platipy.dicom.io.rtstruct_to_nifti.read_dicom_image(dicom_path)
    +

    Read a DICOM image series

    +
    +
    Parameters
    +

    dicom_path (str|pathlib.Path) – Path to the DICOM series to read

    +
    +
    Returns
    +

    The image as a SimpleITK Image

    +
    +
    Return type
    +

    sitk.Image

    +
    +
    +
    +
    +
    +platipy.dicom.io.rtstruct_to_nifti.read_dicom_struct_file(filename)
    +

    Read a DICOM RTSTRUCT file

    +
    +
    Parameters
    +

    filename (str|pathlib.Path) – Path to the RTSTRUCT to read

    +
    +
    Returns
    +

    The RTSTRUCT as a DICOM Dataset

    +
    +
    Return type
    +

    pydicom.Dataset

    +
    +
    +
    +
    +
    +platipy.dicom.io.rtstruct_to_nifti.transform_point_set_from_dicom_struct(dicom_image, dicom_struct, spacing_override=None)
    +

    Converts a set of points from a DICOM RTSTRUCT into a mask array

    +
    +
    Parameters
    +
      +
    • dicom_image (sitk.Image) – The reference image

    • +
    • dicom_struct (pydicom.Dataset) – The DICOM RTSTRUCT

    • +
    • spacing_override (list) – The spacing to override. Defaults to None

    • +
    +
    +
    Returns
    +

    Returns a list of masks and a list of structure names

    +
    +
    Return type
    +

    tuple

    +
    +
    +
    +
    +
    +

    Module contents

    +
    +
    + +
    +
    + + + + + +
    +
    + +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.dicom.nifti_to_rtstruct.html b/docs/site/gen/platipy.dicom.nifti_to_rtstruct.html index 82a0fc8a..967af97b 100644 --- a/docs/site/gen/platipy.dicom.nifti_to_rtstruct.html +++ b/docs/site/gen/platipy.dicom.nifti_to_rtstruct.html @@ -1,252 +1,215 @@ + + + + + + + + + platipy.dicom.nifti_to_rtstruct package - PlatiPy documentation + + + + - - - - - - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
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    +
    + +
    +
    +
    + +

    platipy.dicom.nifti_to_rtstruct package

    -
    +

    Submodules

    -
    -
    -

    platipy.dicom.nifti_to_rtstruct.convert module

    -
    -
    -platipy.dicom.nifti_to_rtstruct.convert.convert_nifti(dcm_file, mask, out_rt_filename, debug=False)
    -
    - -
    -
    -

    platipy.dicom.nifti_to_rtstruct.export_to_rtstruct module

    -
    -
    -platipy.dicom.nifti_to_rtstruct.export_to_rtstruct.copy_attrs_s2t(attrs, source, target, debug)
    -
    - -
    -
    -platipy.dicom.nifti_to_rtstruct.export_to_rtstruct.export_to_rtstruct(params, dat_ct, debug=False)
    -
    - -
    -
    -platipy.dicom.nifti_to_rtstruct.export_to_rtstruct.load_param_file(filename)
    -
    - -
    -
    -

    platipy.dicom.nifti_to_rtstruct.gen_contours module

    -
    -
    -platipy.dicom.nifti_to_rtstruct.gen_contours.gen_contours(mask_file)
    -
    - -
    -
    +
    +
    +

    platipy.dicom.nifti_to_rtstruct.convert module

    +
    +
    +

    platipy.dicom.nifti_to_rtstruct.export_to_rtstruct module

    +
    +
    +

    platipy.dicom.nifti_to_rtstruct.gen_contours module

    +
    +

    Module contents

    -
    -
    - - - - -
    - - -
    - -
    -

    - © Copyright 2020, Phillip Chlap & Rob Finnegan. + + -

    -
    + +
    + + -
    + + +
    + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.dicom.nifti_to_series.html b/docs/site/gen/platipy.dicom.nifti_to_series.html index 265af120..0efcce49 100644 --- a/docs/site/gen/platipy.dicom.nifti_to_series.html +++ b/docs/site/gen/platipy.dicom.nifti_to_series.html @@ -1,239 +1,207 @@ + + + + + + + + + platipy.dicom.nifti_to_series package - PlatiPy documentation + + + + - - - - - - - - - platipy.dicom.nifti_to_series package — PlatiPy documentation - - - + + - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
    +
    + +
    + +
    + +
    +
    + +
    +
    +
    + +
    +

    platipy.dicom.nifti_to_series package

    +
    +

    Submodules

    +
    +
    +

    platipy.dicom.nifti_to_series.convert module

    +
    +
    +

    Module contents

    +
    +
    -
    +
    - -
    -
    -
    -
    - -
    -

    platipy.dicom.nifti_to_series package

    -
    -

    Submodules

    -
    -
    -

    platipy.dicom.nifti_to_series.convert module

    -
    -
    -platipy.dicom.nifti_to_series.convert.convert_nifti_to_dicom_series(image, reference_dcm, tag_overrides=[], output_directory='.')
    -

    Converts a Nifti image to a Dicom image series

    -
    -
    Parameters
    -
      -
    • image (sitk.Image) – A SimpleITK image object to convert

    • -
    • reference_dcm (str) – A directory path containing a reference Dicom series to use

    • -
    • tag_overrides (list, optional) – A list of tags to override containing tuples of -(key, value). Defaults to [].

    • -
    • output_directory (str, optional) – The directory in which to place the generated Dicom -files. Defaults to “.”.

    • + + + +
    +
    -
    - - -
    - -
    -

    - © Copyright 2020, Phillip Chlap & Rob Finnegan. - -

    -
    - -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.dicom.rtstruct_to_nifti.html b/docs/site/gen/platipy.dicom.rtstruct_to_nifti.html index 376e9184..dec70a63 100644 --- a/docs/site/gen/platipy.dicom.rtstruct_to_nifti.html +++ b/docs/site/gen/platipy.dicom.rtstruct_to_nifti.html @@ -1,246 +1,207 @@ + + + + + + + + + platipy.dicom.rtstruct_to_nifti package - PlatiPy documentation + + + + - - - - - - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
    +
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    + +
    + +
    +
    + +
    +
    +
    + +

    platipy.dicom.rtstruct_to_nifti package

    -
    +

    Submodules

    -
    -
    -

    platipy.dicom.rtstruct_to_nifti.convert module

    -
    -
    -platipy.dicom.rtstruct_to_nifti.convert.convert_rtstruct(dcm_img, dcm_rt_file, prefix='Struct_', output_dir='.', output_img=None, spacing=None)
    -
    - -
    -
    -platipy.dicom.rtstruct_to_nifti.convert.fixMissingData(SS)
    -
    - -
    -
    -platipy.dicom.rtstruct_to_nifti.convert.readDICOMImage(filename)
    -
    - -
    -
    -platipy.dicom.rtstruct_to_nifti.convert.readDICOMStructFile(filename)
    -
    - -
    -
    -platipy.dicom.rtstruct_to_nifti.convert.transformPointSetFromDICOMStruct(DICOMImage, DICOMStruct, writeImage, imageOutputName, spacingOverride)
    -
    - -
    -
    +
    +
    +

    platipy.dicom.rtstruct_to_nifti.convert module

    +
    +

    Module contents

    -
    -
    - - - - -
    - - -
    - -
    -

    - © Copyright 2020, Phillip Chlap & Rob Finnegan. + + -

    -
    + +
    + + -
    + + +
    + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.dicom.tests.html b/docs/site/gen/platipy.dicom.tests.html index a0feeecb..04570e2b 100644 --- a/docs/site/gen/platipy.dicom.tests.html +++ b/docs/site/gen/platipy.dicom.tests.html @@ -1,234 +1,279 @@ + + + + + + + + + platipy.dicom.tests package - PlatiPy documentation + + + + - - - - - - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
    +
    + +
    + +
    + +
    +
    + +
    +
    +
    + +

    platipy.dicom.tests package

    -
    +

    Submodules

    -
    -
    -

    platipy.dicom.tests.test_convert_nifti module

    +
    +
    +

    platipy.dicom.tests.test_convert module

    +
    +
    +platipy.dicom.tests.test_convert.dicom_data()
    +

    Gets the data needed for converting DICOM

    +
    +
    Returns
    +

    Path to DICOM data

    +
    +
    Return type
    +

    pathlib.Path

    +
    +
    +
    +
    +
    +platipy.dicom.tests.test_convert.nifti_data()
    +

    Gets the data needed for converting NIFTI

    +
    +
    Returns
    +

    Path to NIFTI data

    +
    +
    Return type
    +

    pathlib.Path

    +
    +
    +
    -
    -platipy.dicom.tests.test_convert_nifti.test_convert_nifti()
    +
    +platipy.dicom.tests.test_convert.test_convert_dicom_to_nifti(dicom_data)
    - -
    -
    -

    platipy.dicom.tests.test_convert_rtstruct module

    -
    -platipy.dicom.tests.test_convert_rtstruct.test_convert_rtstruct()
    +
    +platipy.dicom.tests.test_convert.test_convert_nifti_to_dicom(nifti_data, dicom_data)
    - -
    -
    + +
    +

    platipy.dicom.tests.test_crawl module

    +
    +
    +platipy.dicom.tests.test_crawl.dicom_data()
    +

    Gets the data needed for converting DICOM

    +
    +
    Returns
    +

    Path to DICOM data

    +
    +
    Return type
    +

    pathlib.Path

    +
    +
    +
    +
    +
    +platipy.dicom.tests.test_crawl.nifti_data()
    +

    Gets the data needed for converting NIFTI

    +
    +
    Returns
    +

    Path to NIFTI data

    +
    +
    Return type
    +

    pathlib.Path

    +
    +
    +
    +
    +
    +platipy.dicom.tests.test_crawl.test_convert_dicom_to_nifti(dicom_data)
    +
    +
    +
    +platipy.dicom.tests.test_crawl.test_convert_nifti_to_dicom(nifti_data, dicom_data)
    +
    +
    +

    Module contents

    -
    -
    - - - - -
    - - -
    + + -
    -

    - © Copyright 2020, Phillip Chlap & Rob Finnegan. - -

    -
    + +
    + + -
    + + +
    + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.html b/docs/site/gen/platipy.html index a7b748df..8e085385 100644 --- a/docs/site/gen/platipy.html +++ b/docs/site/gen/platipy.html @@ -1,153 +1,143 @@ + + + + + + + + + platipy package - PlatiPy documentation + + + + - - - - - - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
    +
    + +
    + +
    + +
    +
    + +
    +
    +
    + +

    platipy package

    -
    +

    Subpackages

    -
  • platipy.dicom.dicom_directory_crawler package -
  • platipy.dicom.download package
  • -
  • platipy.dicom.nifti_to_rtstruct package -
  • -
  • platipy.dicom.nifti_to_series package -
  • -
  • platipy.dicom.rtstruct_to_nifti package
  • +

    Module contents

    -
    -
    - - -
    - -
    - - -
    - -
    -

    - © Copyright 2020, Phillip Chlap & Rob Finnegan. + + -

    -
    + +
    + + -
    + + +
    + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.imaging.atlas.html b/docs/site/gen/platipy.imaging.atlas.html index 923b533e..e989892c 100644 --- a/docs/site/gen/platipy.imaging.atlas.html +++ b/docs/site/gen/platipy.imaging.atlas.html @@ -1,279 +1,211 @@ + + + + + + + + + platipy.imaging.atlas package - PlatiPy documentation + + + + - - - - - - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
    +
    + +
    + +
    + +
    +
    + +
    +
    +
    + +
    +

    platipy.imaging.atlas package

    +
    +

    Submodules

    +
    +
    +

    platipy.imaging.atlas.iterative_atlas_removal module

    +
    +
    +

    platipy.imaging.atlas.label_fusion module

    +
    +
    +

    Module contents

    +
    +
    + +
    +
    - - - - -
    -
    +
    +
    +
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.imaging.augment.html b/docs/site/gen/platipy.imaging.augment.html index 7f456e53..0aa8ef41 100644 --- a/docs/site/gen/platipy.imaging.augment.html +++ b/docs/site/gen/platipy.imaging.augment.html @@ -1,275 +1,207 @@ + + + + + + + + + platipy.imaging.augment package - PlatiPy documentation + + + + - - - - - - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
    +
    + +
    + +
    + +
    +
    + +
    +
    +
    + +
    +

    platipy.imaging.augment package

    +
    +

    Submodules

    +
    +
    +

    platipy.imaging.augment.defaug module

    +
    +
    +

    Module contents

    +
    +
    + +
    +
    - - - - -
    -
    +
    +
    +
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.imaging.deformation_fields.html b/docs/site/gen/platipy.imaging.deformation_fields.html index 963852d1..906306b1 100644 --- a/docs/site/gen/platipy.imaging.deformation_fields.html +++ b/docs/site/gen/platipy.imaging.deformation_fields.html @@ -1,401 +1,207 @@ + + + + + + + + + platipy.imaging.deformation_fields package - PlatiPy documentation + + + + - - - - - - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
    +
    + +
    + +
    + +
    +
    + +
    +
    +
    + +
    +

    platipy.imaging.deformation_fields package

    +
    +

    Submodules

    +
    +
    +

    platipy.imaging.deformation_fields.deformation_field_operations module

    +
    +
    +

    Module contents

    +
    +
    + +
    +
    - - - - -
    -
    +
    +
    +
    -
    - - -
    - -
    -

    - © Copyright 2020, Phillip Chlap & Rob Finnegan. - -

    -
    - -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.imaging.generation.html b/docs/site/gen/platipy.imaging.generation.html new file mode 100644 index 00000000..a2c72457 --- /dev/null +++ b/docs/site/gen/platipy.imaging.generation.html @@ -0,0 +1,505 @@ + + + + + + + + + platipy.imaging.generation package - PlatiPy documentation + + + + + + + + + + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
    +
    + +
    + +
    + +
    +
    + +
    +
    +
    + +
    +

    platipy.imaging.generation package

    +
    +

    Submodules

    +
    +
    +

    platipy.imaging.generation.augment module

    +
    +
    +class platipy.imaging.generation.augment.ContractAugment(mask, vector_contract=10, 10, 10, gaussian_smooth=5, bone_mask=False)
    +

    Bases: platipy.imaging.generation.augment.DeformableAugment

    +
    +
    +augment()
    +
    +
    +
    +
    +class platipy.imaging.generation.augment.DeformableAugment
    +

    Bases: abc.ABC

    +
    +
    +abstract augment()
    +
    +
    +
    +
    +class platipy.imaging.generation.augment.ExpandAugment(mask, vector_expand=10, 10, 10, gaussian_smooth=5, bone_mask=False)
    +

    Bases: platipy.imaging.generation.augment.DeformableAugment

    +
    +
    +augment()
    +
    +
    +
    +
    +class platipy.imaging.generation.augment.ShiftAugment(mask, vector_shift=10, 10, 10, gaussian_smooth=5)
    +

    Bases: platipy.imaging.generation.augment.DeformableAugment

    +
    +
    +augment()
    +
    +
    +
    +
    +platipy.imaging.generation.augment.apply_augmentation(image, augmentation, masks=[])
    +
    +
    +
    +platipy.imaging.generation.augment.generate_random_augmentation(ct_image, masks)
    +
    +
    +
    +

    platipy.imaging.generation.dvf module

    +
    +
    +platipy.imaging.generation.dvf.generate_field_asymmetric_contract(mask_image, vector_asymmetric_contract=10, 10, 10, gaussian_smooth=5)
    +

    Contracts a structure (defined using a binary mask) using a specified vector.

    +
    +
    Parameters
    +
      +
    • mask_image ([SimpleITK.Image]) – The binary mask to contract.

    • +
    • vector_asymmetric_contract (tuple, optional) – The contraction vector applied to the entire +binary mask. +Convention: (+/-, +/-, +/-) = (sup/inf, +post/ant, left/right) border is contracted. +Defined in millimetres. +Defaults to (10, 10, 10).

    • +
    • gaussian_smooth (int | list, optional) – Scale of a Gaussian kernel used to smooth the +deformation vector field. Defaults to 5.

    • +
    +
    +
    Returns
    +

    The binary mask following the contract. +[SimpleITK.DisplacementFieldTransform]: The transform representing the contract. +[SimpleITK.Image]: The displacement vector field representing the contract.

    +
    +
    Return type
    +

    [SimpleITK.Image]

    +
    +
    +
    +
    +
    +platipy.imaging.generation.dvf.generate_field_asymmetric_extend(mask_image, vector_asymmetric_extend=10, 10, 10, gaussian_smooth=5)
    +

    Extends a structure (defined using a binary mask) using a specified vector.

    +
    +
    Parameters
    +
      +
    • mask_image ([SimpleITK.Image]) – The binary mask to extend.

    • +
    • vector_asymmetric_extend (tuple, optional) – The extension vector applied to the entire +binary mask. +Convention: (+/-, +/-, +/-) = (sup/inf, +post/ant, left/right) border is extended. +Defined in millimetres. +Defaults to (10, 10, 10).

    • +
    • gaussian_smooth (int | list, optional) – Scale of a Gaussian kernel used to smooth the +deformation vector field. Defaults to 5.

    • +
    +
    +
    Returns
    +

    The binary mask following the extension. +[SimpleITK.DisplacementFieldTransform]: The transform representing the extension. +[SimpleITK.Image]: The displacement vector field representing the extension.

    +
    +
    Return type
    +

    [SimpleITK.Image]

    +
    +
    +
    +
    +
    +platipy.imaging.generation.dvf.generate_field_expand(mask, bone_mask=False, expand=3, gaussian_smooth=5, use_internal_deformation=True)
    +

    Expands a structure (defined using a binary mask) using a specified vector to define the +dilation kernel.

    +
    +
    Parameters
    +
      +
    • mask ([SimpleITK.Image]) – The binary mask to expand.

    • +
    • bone_mask ([SimpleITK.Image, optional]) – A binary mask defining regions where we expect +restricted deformations.

    • +
    • vector_asymmetric_extend (int |tuple, optional) –

      +
      The expansion vector applied to the entire

      binary mask.

      +
      +
      +

      Convention: (z,y,x) size of expansion kernel. +Defined in millimetres. +Defaults to 3.

      +

    • +
    • gaussian_smooth (int | list, optional) – Scale of a Gaussian kernel used to smooth the +deformation vector field. Defaults to 5.

    • +
    +
    +
    Returns
    +

    The binary mask following the expansion. +[SimpleITK.DisplacementFieldTransform]: The transform representing the expansion. +[SimpleITK.Image]: The displacement vector field representing the expansion.

    +
    +
    Return type
    +

    [SimpleITK.Image]

    +
    +
    +
    +
    +
    +platipy.imaging.generation.dvf.generate_field_radial_bend(reference_image, body_mask, reference_point, axis_of_rotation=[0, 0, - 1], scale=0.1, mask_bend_from_reference_point='z', 'inf', gaussian_smooth=5)
    +

    Generates a synthetic field characterised by radial bending. +Typically, this field would be used to simulate a moving head and so masking is important.

    +
    +
    Parameters
    +
      +
    • reference_image ([SimpleITK.Image]) – The image to be deformed.

    • +
    • body_mask ([SimpleITK.Image]) – A binary mask in which the deformation field will be defined

    • +
    • reference_point ([tuple]) – The point (z,y,x) about which the rotation field is defined.

    • +
    • axis_of_rotation (tuple, optional) – The axis of rotation (z,y,x). Defaults to [0, 0, -1].

    • +
    • scale (int, optional) – The deformation vector length at each point will equal scale +multiplied by the distance to that point from reference_point. +Defaults to 1.

    • +
    • mask_bend_from_reference_point (tuple, optional) – The dimension (z=axial, y=coronal, +x=sagittal) and limit (inf/sup, post/ant, +left/right) for masking the vector field, +relative to the reference point. Defaults +to (“z”, “inf”).

    • +
    • gaussian_smooth (int | list, optional) – Scale of a Gaussian kernel used to smooth the +deformation vector field. Defaults to 5.

    • +
    +
    +
    Returns
    +

    The binary mask following the expansion. +[SimpleITK.DisplacementFieldTransform]: The transform representing the expansion. +[SimpleITK.Image]: The displacement vector field representing the expansion.

    +
    +
    Return type
    +

    [SimpleITK.Image]

    +
    +
    +
    +
    +
    +platipy.imaging.generation.dvf.generate_field_shift(mask_image, vector_shift=10, 10, 10, gaussian_smooth=5)
    +

    Shifts (moves) a structure defined using a binary mask.

    +
    +
    Parameters
    +
      +
    • mask_image ([SimpleITK.Image]) – The binary mask to shift.

    • +
    • vector_shift (tuple, optional) – The displacement vector applied to the entire binary mask. +Convention: (+/-, +/-, +/-) = (sup/inf, post/ant, +left/right) shift. +Defined in millimetres. +Defaults to (10, 10, 10).

    • +
    • gaussian_smooth (int | list, optional) – Scale of a Gaussian kernel used to smooth the +deformation vector field. Defaults to 5.

    • +
    +
    +
    Returns
    +

    The binary mask following the shift. +[SimpleITK.DisplacementFieldTransform]: The transform representing the shift. +[SimpleITK.Image]: The displacement vector field representing the shift.

    +
    +
    Return type
    +

    [SimpleITK.Image]

    +
    +
    +
    +
    +
    +

    platipy.imaging.generation.image module

    +
    +
    +platipy.imaging.generation.image.insert_sphere(arr, sp_radius=4, sp_centre=0, 0, 0)
    +

    Insert a sphere into the give array

    +
    +
    Parameters
    +
      +
    • arr (np.array) – Array in which to insert sphere

    • +
    • sp_radius (int, optional) – The radius of the sphere. Defaults to 4.

    • +
    • sp_centre (tuple, optional) – The position at which the sphere should be inserted. Defaults +to (0, 0, 0).

    • +
    +
    +
    Returns
    +

    An array with the sphere inserted

    +
    +
    Return type
    +

    np.array

    +
    +
    +
    +
    +
    +platipy.imaging.generation.image.insert_sphere_image(image, sp_radius, sp_centre)
    +

    Insert a sphere into a blank image with the same size as image

    +
    +
    Parameters
    +
      +
    • image (sitk.Image) – Image in which to insert sphere

    • +
    • sp_radius (int | list, optional) – The radius of the sphere. Can also be defined as a vector. Defaults to 4.

    • +
    • sp_centre (tuple, optional) – The position at which the sphere should be inserted. Defaults +to (0, 0, 0).

    • +
    +
    +
    Returns
    +

    An array with the sphere inserted

    +
    +
    Return type
    +

    np.array

    +
    +
    +
    +
    +
    +

    platipy.imaging.generation.mask module

    +
    +
    +platipy.imaging.generation.mask.get_bone_mask(image, lower_threshold=350, upper_threshold=3500, max_hole_size=5)
    +

    Automatically generate a binary mask of bones from a CT image.

    +
    +
    Parameters
    +
      +
    • image ([SimpleITK.Image]) – The patient x-ray CT image to segment.

    • +
    • lower_threshold (int, optional) – Lower voxel value for threshold. Defaults to 350.

    • +
    • upper_threshold (int, optional) – Upper voxel value for threshold. Defaults to 3500.

    • +
    • max_hole_size (int | list | bool, optional) – Maximum hole size to be filled in millimetres. +Can be specified as a vector (z,y,x). Defaults +to 5.

    • +
    +
    +
    Returns
    +

    The binary bone mask.

    +
    +
    Return type
    +

    [SimpleITK.Image]

    +
    +
    +
    +
    +
    +platipy.imaging.generation.mask.get_external_mask(image, lower_threshold=- 100, upper_threshold=2500, dilate=1, max_hole_size=False)
    +

    Automatically generate a binary mask of the patient external contour. +Uses slice-wise convex hull generation.

    +
    +
    Parameters
    +
      +
    • image ([SimpleITK.Image]) – The patient x-ray CT image to segment. May work with other +modalities with modified thresholds.

    • +
    • lower_threshold (int, optional) – Lower voxel value for threshold. Defaults to -100.

    • +
    • upper_threshold (int, optional) – Upper voxel value for threshold. Defaults to 2500.

    • +
    • dilate (int | list | bool, optional) – Dilation filter size applied to the binary mask. Can +be specified as a vector (z,y,x). Defaults to 1.

    • +
    • max_hole_size (int | list | bool, optional) – Maximum hole size to be filled in +millimetres. Can be specified as a vector +(z,y,x). Defaults to False.

    • +
    +
    +
    Returns
    +

    The binary external mask.

    +
    +
    Return type
    +

    [SimpleITK.Image]

    +
    +
    +
    +
    +
    +

    Module contents

    +
    +
    + +
    +
    + + + + + +
    +
    + +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.imaging.html b/docs/site/gen/platipy.imaging.html index 717ac775..c448f557 100644 --- a/docs/site/gen/platipy.imaging.html +++ b/docs/site/gen/platipy.imaging.html @@ -1,179 +1,162 @@ + + + + + + + + + platipy.imaging package - PlatiPy documentation + + + + - - - - - - - - - platipy.imaging package — PlatiPy documentation - - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
    +
    + +
    + +
    + +
    +
    + +
    +
    +
    + +

    platipy.imaging package

    -
    +

    Subpackages

    -
    -
    +
    +

    Module contents

    -
    -
    - - - - -
    - - -
    + + -
    -

    - © Copyright 2020, Phillip Chlap & Rob Finnegan. - -

    -
    + +
    + + -
    + + +
    + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.imaging.label.html b/docs/site/gen/platipy.imaging.label.html index 86e24719..dcb201f2 100644 --- a/docs/site/gen/platipy.imaging.label.html +++ b/docs/site/gen/platipy.imaging.label.html @@ -1,157 +1,147 @@ + + + + + + + + + platipy.imaging.label package - PlatiPy documentation + + + + - - - - - - - - - platipy.imaging.label package — PlatiPy documentation - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
    +
    + +
    + +
    + +
    +
    + +
    +
    +
    + +

    platipy.imaging.label package

    -
    +

    Submodules

    -
    -
    -

    platipy.imaging.label.label_comparison module

    +
    +
    +

    platipy.imaging.label.comparison module

    Module name: LabelComparison Author: Robert Finnegan Date: December 2018 @@ -162,123 +152,315 @@

    Submodules -
    -platipy.imaging.label.label_comparison.compute_metric_dsc(imA, imB)
    -
    - +
    +platipy.imaging.label.comparison.compute_metric_dsc(label_a, label_b)
    +

    Compute the Dice Similarity Coefficient between two labels

    +
    +
    Parameters
    +
      +
    • label_a (sitk.Image) – A mask to compare

    • +
    • label_b (sitk.Image) – Another mask to compare

    • +
    +
    +
    Returns
    +

    The Dice Similarity Coefficient

    +
    +
    Return type
    +

    float

    +
    +
    +
    -
    -platipy.imaging.label.label_comparison.compute_metric_hd(imFixed, imMoving, verbose=False)
    -
    - +
    +platipy.imaging.label.comparison.compute_metric_hd(label_a, label_b)
    +

    Compute the Hausdorff distance between two labels

    +
    +
    Parameters
    +
      +
    • label_a (sitk.Image) – A mask to compare

    • +
    • label_b (sitk.Image) – Another mask to compare

    • +
    +
    +
    Returns
    +

    The maximum Hausdorff distance

    +
    +
    Return type
    +

    float

    +
    +
    +
    -
    -platipy.imaging.label.label_comparison.compute_metric_masd(imFixed, imMoving)
    -
    - +
    +platipy.imaging.label.comparison.compute_metric_masd(label_a, label_b)
    +

    Compute the mean absolute distance between two labels

    +
    +
    Parameters
    +
      +
    • label_a (sitk.Image) – A mask to compare

    • +
    • label_b (sitk.Image) – Another mask to compare

    • +
    +
    +
    Returns
    +

    The mean absolute surface distance

    +
    +
    Return type
    +

    float

    +
    +
    +
    -
    -platipy.imaging.label.label_comparison.compute_metric_sensitivity(imFixed, imMoving)
    -
    - +
    +platipy.imaging.label.comparison.compute_metric_sensitivity(label_a, label_b)
    +

    Compute the sensitivity between two labels

    +
    +
    Parameters
    +
      +
    • label_a (sitk.Image) – A mask to compare

    • +
    • label_b (sitk.Image) – Another mask to compare

    • +
    +
    +
    Returns
    +

    The sensitivity between the two labels

    +
    +
    Return type
    +

    float

    +
    +
    +
    -
    -platipy.imaging.label.label_comparison.compute_metric_specificity(imFixed, imMoving)
    -
    - +
    +platipy.imaging.label.comparison.compute_metric_specificity(label_a, label_b)
    +

    Compute the specificity between two labels

    +
    +
    Parameters
    +
      +
    • label_a (sitk.Image) – A mask to compare

    • +
    • label_b (sitk.Image) – Another mask to compare

    • +
    +
    +
    Returns
    +

    The specificity between the two labels

    +
    +
    Return type
    +

    float

    +
    +
    +
    -
    -platipy.imaging.label.label_comparison.compute_surface_metrics(imFixed, imMoving, verbose=False)
    -

    HD, meanSurfDist, medianSurfDist, maxSurfDist, stdSurfDist

    +
    +platipy.imaging.label.comparison.compute_surface_metrics(label_a, label_b, verbose=False)
    +

    Compute surface distance metrics between two labels. Surface metrics computed are: +hausdorffDistance, meanSurfaceDistance, medianSurfaceDistance, maximumSurfaceDistance, +sigmaSurfaceDistance

    +
    +
    Parameters
    +
      +
    • label_a (sitk.Image) – A mask to compare

    • +
    • label_b (sitk.Image) – Another mask to compare

    • +
    • verbose (bool, optional) – Whether to print verbose output. Defaults to False.

    • +
    +
    +
    Returns
    +

    Dictionary object containing surface distance metrics

    +
    +
    Return type
    +

    dict

    +
    +
    -
    -
    -platipy.imaging.label.label_comparison.compute_volume_metrics(imFixed, imMoving)
    -

    DSC, VolOverlap, FracOverlap, TruePosFrac, TrueNegFrac, FalsePosFrac, FalseNegFrac

    +
    +platipy.imaging.label.comparison.compute_volume_metrics(label_a, label_b)
    +

    Compute volume metrics between two labels. Volume metrics computed are: +DSC, volumeOverlap fractionOverlap truePositiveFraction trueNegativeFraction +falsePositiveFraction falseNegativeFraction

    +
    +
    Parameters
    +
      +
    • label_a (sitk.Image) – A mask to compare

    • +
    • label_b (sitk.Image) – Another mask to compare

    • +
    +
    +
    Returns
    +

    Dictionary object containing volume metrics

    +
    +
    Return type
    +

    dict

    +
    +
    +
    +

    +
    +

    platipy.imaging.label.fusion module

    +
    +
    +platipy.imaging.label.fusion.combine_labels(atlas_set, structure_name, label='DIR', threshold=0.0001, smooth_sigma=1.0)
    +

    Combine labels using weight maps

    - -
    -
    -

    platipy.imaging.label.label_operations module

    -
    -platipy.imaging.label.label_operations.morphological_interpolate(sitk_image, copy_info=True)
    -

    Performs morphological interpolation -See: https://github.com/KitwareMedical/ITKMorphologicalContourInterpolation

    -

    Useful for filling in gaps in contouring between slices

    +
    +platipy.imaging.label.fusion.combine_labels_staple(label_list_dict, threshold=0.0001)
    +

    Combine labels using STAPLE

    -
    -
    -platipy.imaging.label.label_operations.process_probability_image(probability_image, threshold=0.5)
    +
    +platipy.imaging.label.fusion.compute_weight_map(target_image, moving_image, vote_type='unweighted', vote_params={'blockSize': 5, 'epsilon': 1e-05, 'factor': 1000000000000.0, 'gain': 6, 'sigma': 2.0})
    +

    Computes the weight map

    +
    +
    +
    +platipy.imaging.label.fusion.process_probability_image(probability_image, threshold=0.5)

    Generate a mask given a probability image, performing some basic post processing as well.

    - -
    -
    + +
    +

    platipy.imaging.label.iar module

    +
    +
    +platipy.imaging.label.iar.gaussian_curve(x, a, m, s)
    +

    Returns a Gaussian (normal) curve

    +
    +
    Parameters
    +
      +
    • x (np.ndarray) – values to sample the normal curve

    • +
    • a (float) – magnitude

    • +
    • m (float) – location (mean)

    • +
    • s (float) – scale (standard deviation)

    • +
    +
    +
    Returns
    +

    sampled values along the normal curve

    +
    +
    Return type
    +

    np.ndarray

    +
    +
    +
    +
    +
    +platipy.imaging.label.iar.median_absolute_deviation(data, axis=None)
    +

    Median Absolute Deviation: a “Robust” version of standard deviation. +Indices variabililty of the sample. +https://en.wikipedia.org/wiki/Median_absolute_deviation

    +
    +
    +
    +platipy.imaging.label.iar.run_iar(atlas_set, reference_structure, smooth_distance_maps=False, smooth_sigma=1, z_score_statistic='MAD', outlier_method='IQR', min_best_atlases=10, outlier_factor=1.5, iteration=0, single_step=False, project_on_sphere=False, label='DIR')
    +

    Perform iterative atlas removal on the atlas_set

    +
    +
    +
    +

    platipy.imaging.label.projection module

    +
    +
    +platipy.imaging.label.projection.evaluate_distance_on_surface(reference_volume, test_volume, abs_distance=True, reference_as_distance_map=False)
    +

    Evaluates a distance map on a surface +Input: reference_volume: binary volume SimpleITK image, or alternatively a distance map

    +
    +

    test_volume: binary volume SimpleITK image

    +
    +

    Output: theta, phi, values

    +
    +
    +
    +platipy.imaging.label.projection.evaluate_distance_to_reference(reference_volume, test_volume, resample_factor=1)
    +

    Evaluates the distance from the surface of a test volume to a reference +Input: reference_volume: binary volume SimpleITK image

    +
    +

    test_volume: binary volume SimpleITK image

    +
    +

    Output: values : the distance to each point on the reference volume surface

    +
    +
    +
    +platipy.imaging.label.projection.regrid_spherical_data(theta, phi, values, resolution)
    +

    Re-grids spherical data +Input: theta, phi, values +Options: plot a figure (plotFig), save a figure (saveFig), case identifier (figName) +Output: p_lat, p_long, grid_values (, fig)

    +
    +
    +
    +

    platipy.imaging.label.utils module

    +
    +
    +platipy.imaging.label.utils.get_com(label, real_coords=False)
    +

    Get centre of mass of a SimpleITK.Image

    +
    +
    +
    +platipy.imaging.label.utils.vectorised_transform_index_to_physical_point(image, point_array, rotate=True)
    +

    Transforms a set of points from array indices to real-space

    +
    +
    +
    +platipy.imaging.label.utils.vectorised_transform_physical_point_to_index(image, point_array, rotate=True)
    +

    Transforms a set of points from real-space to array indices

    +
    +
    +

    Module contents

    -
    -
    - + + - - -
    - - -
    - -
    -

    - © Copyright 2020, Phillip Chlap & Rob Finnegan. - -

    -
    + +
    + + -
    + + +
    + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.imaging.projects.bronchus.html b/docs/site/gen/platipy.imaging.projects.bronchus.html index 17cbc8da..112c5baf 100644 --- a/docs/site/gen/platipy.imaging.projects.bronchus.html +++ b/docs/site/gen/platipy.imaging.projects.bronchus.html @@ -1,156 +1,146 @@ + + + + + + + + + platipy.imaging.projects.bronchus package - PlatiPy documentation + + + + - - - - - - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
    +
    + +
    + +
    + +
    +
    + +
    +
    +
    + +

    platipy.imaging.projects.bronchus package

    -
    +

    Submodules

    -
    -
    +
    +

    platipy.imaging.projects.bronchus.bronchus module

    Created on Wed Feb 21 16:39:27 2018

    Bronchus segmentation. The superior extent of bronchus is around 4cm SUP from Carina (From first @@ -161,7 +151,7 @@

    Submodules
    platipy.imaging.projects.bronchus.bronchus.fast_mask(img, start, end)
    @@ -177,7 +167,6 @@

    Submodules
    platipy.imaging.projects.bronchus.bronchus.generate_airway_mask(dest, img, lung_mask, config_dict=None)
    @@ -191,7 +180,6 @@

    Submodules
    platipy.imaging.projects.bronchus.bronchus.generate_lung_mask(img)
    @@ -208,7 +196,6 @@

    Submodules
    platipy.imaging.projects.bronchus.bronchus.get_distance(a_mask, b_mask)
    @@ -226,9 +213,8 @@

    Submodules +

    +

    platipy.imaging.projects.bronchus.run module

    @@ -250,80 +236,76 @@

    Submodules +

    +

    platipy.imaging.projects.bronchus.service module

    platipy.imaging.projects.bronchus.service.bronchus_service(data_objects, working_dir, settings)

    Implements the platipy framework to provide bronchus segmentation.

    - -
    -
    + +

    Module contents

    -
    -
    - - - - -
    - - -
    + + -
    -

    - © Copyright 2020, Phillip Chlap & Rob Finnegan. - -

    -
    + +
    + + -
    + + +
    + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.imaging.projects.cardiac.html b/docs/site/gen/platipy.imaging.projects.cardiac.html index f7b639a6..9f6e612c 100644 --- a/docs/site/gen/platipy.imaging.projects.cardiac.html +++ b/docs/site/gen/platipy.imaging.projects.cardiac.html @@ -1,160 +1,150 @@ + + + + + + + + + platipy.imaging.projects.cardiac package - PlatiPy documentation + + + + - - - - - - - - - platipy.imaging.projects.cardiac package — PlatiPy documentation - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
    +
    + +
    + +
    + +
    +
    + +
    +
    +
    + +

    platipy.imaging.projects.cardiac package

    -
    +

    Submodules

    -
    -
    +
    +

    platipy.imaging.projects.cardiac.run module

    -platipy.imaging.projects.cardiac.run.run_cardiac_segmentation(img, settings={'IARSettings': {'minBestAtlases': 5, 'outlierFactor': 1.5, 'outlierMethod': 'IQR', 'project_on_sphere': False, 'referenceStructure': 'WHOLEHEART', 'smoothDistanceMaps': True, 'smoothSigma': 1, 'zScoreStatistic': 'MAD'}, 'atlasSettings': {'atlasIdList': ['13', '17', '33', '12', '16', '22', '27'], 'atlasImageFormat': 'Case_{0}/Images/Case_{0}_CROP.nii.gz', 'atlasLabelFormat': 'Case_{0}/Structures/Case_{0}_{1}_CROP.nii.gz', 'atlasPath': '/atlas', 'atlasStructures': ['WHOLEHEART', 'LANTDESCARTERY_SPLINE'], 'autoCropAtlas': True}, 'autoCropSettings': {'expansion': [2, 2, 2]}, 'deformableSettings': {'isotropicResample': True, 'iterationStaging': [5, 5, 5], 'ncores': 8, 'resolutionStaging': [16, 8, 2], 'smoothingSigmas': [0, 0, 0], 'trace': False}, 'labelFusionSettings': {'optimalThreshold': {'WHOLEHEART': 0.44}, 'voteParams': {}, 'voteType': 'unweighted'}, 'outputFormat': 'Auto_{0}.nii.gz', 'returnAsCropped': False, 'rigidSettings': {'guideStructure': False, 'initialReg': 'Similarity', 'options': {'default_value': - 1024, 'final_interp': 3, 'metric': 'mean_squares', 'number_of_iterations': 50, 'optimiser': 'gradient_descent_line_search', 'sampling_rate': 0.75, 'shrink_factors': [16, 8, 4], 'smooth_sigmas': [0, 0, 0]}, 'trace': False}, 'vesselSpliningSettings': {'spliningDirection': {'LANTDESCARTERY_SPLINE': 'z'}, 'stopCondition': {'LANTDESCARTERY_SPLINE': 'count'}, 'stopConditionValue': {'LANTDESCARTERY_SPLINE': 1}, 'vesselNameList': ['LANTDESCARTERY_SPLINE'], 'vesselRadius_mm': {'LANTDESCARTERY_SPLINE': 2}}})
    +platipy.imaging.projects.cardiac.run.run_cardiac_segmentation(img, guide_structure=None, settings={'atlas_settings': {'atlas_id_list': ['13', '17', '33', '12', '16', '22', '27'], 'atlas_image_format': 'Case_{0}/Images/Case_{0}_CROP.nii.gz', 'atlas_label_format': 'Case_{0}/Structures/Case_{0}_{1}_CROP.nii.gz', 'atlas_path': '/atlas', 'atlas_structure_list': ['WHOLEHEART', 'LANTDESCARTERY_SPLINE'], 'crop_atlas_expansion_mm': 10, 10, 10, 'crop_atlas_to_structures': False, 'guide_structure_name': 'WHOLEHEART'}, 'auto_crop_target_image_settings': {'expansion_mm': [2, 2, 2]}, 'deformable_registration_settings': {'default_value': - 1000, 'isotropic_resample': True, 'iteration_staging': [5, 5, 5], 'ncores': 8, 'resolution_staging': [16, 8, 2], 'smoothing_sigmas': [0, 0, 0], 'verbose': False}, 'iar_settings': {'min_best_atlases': 5, 'outlier_factor': 1.5, 'outlier_method': 'iqr', 'project_on_sphere': False, 'reference_structure': 'WHOLEHEART', 'smooth_distance_maps': True, 'smooth_sigma': 1, 'z_score_statistic': 'mad'}, 'label_fusion_settings': {'optimal_threshold': {'WHOLEHEART': 0.5}, 'vote_params': {}, 'vote_type': 'unweighted'}, 'linear_registration_settings': {'default_value': - 1024, 'metric': 'mean_squares', 'number_of_iterations': 50, 'optimiser': 'gradient_descent_line_search', 'reg_method': 'similarity', 'sampling_rate': 0.75, 'shrink_factors': [16, 8, 4], 'smooth_sigmas': [0, 0, 0], 'verbose': False}, 'return_additional_probability_dict': True, 'return_as_cropped': False, 'structure_guided_registration_settings': {'default_value': 0, 'isotropic_resample': True, 'iteration_staging': [25, 25, 25], 'ncores': 8, 'resolution_staging': [16, 8, 2], 'smoothing_sigmas': [0, 0, 0], 'verbose': False}, 'vessel_spline_settings': {'scan_direction_dict': {'LANTDESCARTERY_SPLINE': 'z'}, 'stop_condition_type_dict': {'LANTDESCARTERY_SPLINE': 'count'}, 'stop_condition_value_dict': {'LANTDESCARTERY_SPLINE': 1}, 'vessel_name_list': ['LANTDESCARTERY_SPLINE'], 'vessel_radius_mm_dict': {'LANTDESCARTERY_SPLINE': 2}}})

    Runs the atlas-based cardiac segmentation

    Parameters
    @@ -172,189 +162,70 @@

    Submodules -

    platipy.imaging.projects.cardiac.service module

    -
    -
    -platipy.imaging.projects.cardiac.service.cardiac_service(data_objects, working_dir, settings)
    -

    Implements the platipy framework to provide cardiac atlas based segmentation.

    -
    - -

    -
    -

    platipy.imaging.projects.cardiac.utils module

    -
    -
    -platipy.imaging.projects.cardiac.utils.AutoLungSegment(image, l=0.05, u=0.4, NPthresh=100000.0)
    -

    Segments the lungs, generating a bounding box

    -
    -
    Args

    image (sitk.Image) : the input image -l (float) : the lower (normalised) threshold -u (float) : the upper (normalised) threshold -NPthresh (int) : lower limit of voxel counts for a structure to be tested

    -
    -
    Returns

    maskBox (np.ndarray) : bounding box of the automatically segmented lungs -maskBinary (sitk.Image) : the segmented lungs (+/- airways)

    -
    -
    -
    - -
    -
    -platipy.imaging.projects.cardiac.utils.COMFromImageList(sitkImageList, conditionType='count', conditionValue=0, scanDirection='z', debug=False)
    -
    -
    Input: list of SimpleITK images

    minimum total slice area required for the tube to be inserted at that slice -scan direction: x = sagittal, y=coronal, z=axial

    -
    -
    -

    Output: mean centre of mass positions, with shape (NumSlices, 2) -Note: positions are converted into image space by default

    -
    - -
    -
    -platipy.imaging.projects.cardiac.utils.ConvertSimpleITKtoVTK(img)
    -
    - -
    -
    -platipy.imaging.projects.cardiac.utils.CropImage(image, cropBox)
    -

    Crops an image using a bounding box

    -
    -
    Args

    image (sitk.Image) : the input image -cropBox (list, np.ndarray) : the bounding box

    -
    -

    (sag0, cor0, ax0, sagD, corD, axD)

    -
    -
    -
    Returns

    imCrop (sitk.Image) : the cropped image

    -
    -
    -
    - -
    -
    -platipy.imaging.projects.cardiac.utils.SimpleITKImageFromVTKTube(tube, SITKReferenceImage, debug=False)
    -

    Input: VTK tube, referenceImage (used for spacing, etc.) -Output: SimpleITK image -Note: Uses binary output (background 0, foreground 1)

    -
    - -
    -
    -platipy.imaging.projects.cardiac.utils.ThresholdAndMeasureLungVolume(image, l=0, u=1)
    -

    Thresholds an image using upper and lower bounds in the intensity. -Each non-connected component has the volume and perimeter/border ratio measured

    -
    -
    Args

    image (sitk.Image) : the input image -l (float) : the lower threshold -u (float) : the upper threshold

    -
    -
    Returns

    NP (np.ndarray) : a one-dimensional array of the number of pixels in each component -PBR (np.ndarray) : a one-dimensional array of the perimeter/border ratio for each component -mask (sitk.Image) : the connected component label map -maxVals (np.ndarray): a one-dimensional array of the label map values

    -
    -
    -
    - -
    -
    -platipy.imaging.projects.cardiac.utils.norm(x, mean, sd)
    -
    - -
    -
    -platipy.imaging.projects.cardiac.utils.res(p, y, x)
    -
    - -
    -
    -platipy.imaging.projects.cardiac.utils.tubeFromCOMList(COMList, radius, debug=False)
    -

    Input: image-space positions along the tube centreline. -Output: VTK tube -Note: positions do not have to be continuous - the tube is interpolated in real space

    -
    - -
    -
    -platipy.imaging.projects.cardiac.utils.vesselSplineGeneration(referenceImage, atlasSet, vesselNameList, vesselRadiusDict, stopConditionTypeDict, stopConditionValueDict, scanDirectionDict, debug=False)
    -
    - -
    -
    -platipy.imaging.projects.cardiac.utils.writeVTKTubeToFile(tube, filename)
    -

    Input: VTK tube -Output: exit success -Note: format is XML VTP

    -
    - -
    -
    + +
    +

    platipy.imaging.projects.cardiac.service module

    +
    +

    Module contents

    -
    -
    - + + - - -
    - - -
    - -
    -

    - © Copyright 2020, Phillip Chlap & Rob Finnegan. - -

    -
    + +
    + + -
    + + +
    + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.imaging.projects.html b/docs/site/gen/platipy.imaging.projects.html index 93575726..7558fc35 100644 --- a/docs/site/gen/platipy.imaging.projects.html +++ b/docs/site/gen/platipy.imaging.projects.html @@ -1,153 +1,143 @@ + + + + + + + + + platipy.imaging.projects package - PlatiPy documentation + + + + - - - - - - - - - platipy.imaging.projects package — PlatiPy documentation - - - + + - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
    +
    + +
    + +
    + +
    +
    + +
    + -
    - + + - - -
    - - -
    - -
    -

    - © Copyright 2020, Phillip Chlap & Rob Finnegan. - -

    -
    + +
    + + -
    + + +
    + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.imaging.registration.html b/docs/site/gen/platipy.imaging.registration.html index e0941895..a4a1319a 100644 --- a/docs/site/gen/platipy.imaging.registration.html +++ b/docs/site/gen/platipy.imaging.registration.html @@ -1,230 +1,238 @@ + + + + + + + + + platipy.imaging.registration package - PlatiPy documentation + + + + - - - - - - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
    +
    + +
    + +
    + +
    +
    + +
    +
    +
    + +

    platipy.imaging.registration package

    -
    +

    Submodules

    -
    -
    -

    platipy.imaging.registration.registration module

    -
    -
    -platipy.imaging.registration.registration.alignment_registration(fixed_image, moving_image, default_value=0, moments=True)
    -
    - +
    +
    +

    platipy.imaging.registration.deformable module

    -
    -platipy.imaging.registration.registration.apply_field(input_image, transform, structure=False, default_value=- 1024, interp=1)
    -

    Transform a volume of structure with the given deformation field.

    -
    -
    Args

    input_image (sitk.Image) : the image to transform -transform (sitk.Transform) : the transform to apply to the structure or mask -structure (bool) : if true, the input will be treated as a struture, as a volume otherwise -interp (int) : the type of interpolation to use, eg. sitk.sitkNearestNeighbor

    -
    -
    Returns

    resampled_image (sitk.Image) : the transformed image

    -
    -
    -
    - -
    -
    -platipy.imaging.registration.registration.bspline_registration(fixed_image, moving_image, moving_structure=False, fixed_structure=False, options={'default_value': - 1024, 'grid_scale_factors': [1, 2, 4], 'initial_grid_spacing': 64, 'interp_order': 3, 'metric': 'correlation', 'number_of_iterations': 20, 'optimiser': 'LBFGS', 'resolution_staging': [8, 4, 2], 'sampling_rate': 0.1, 'smooth_sigmas': [4, 2, 1]}, isotropic_resample=False, initial_isotropic_size=1, initial_isotropic_smooth_scale=0, trace=False, ncores=8, debug=False)
    +
    +platipy.imaging.registration.deformable.bspline_registration(fixed_image, moving_image, fixed_structure=False, moving_structure=False, resolution_staging=[8, 4, 2], smooth_sigmas=[4, 2, 1], sampling_rate=0.1, optimiser='LBFGS', metric='mean_squares', initial_grid_spacing=64, grid_scale_factors=[1, 2, 4], interp_order=3, default_value=- 1000, number_of_iterations=20, isotropic_resample=False, initial_isotropic_size=1, number_of_histogram_bins_mi=30, verbose=False, ncores=8)

    B-Spline image registration using ITK

    IMPORTANT - THIS IS UNDER ACTIVE DEVELOPMENT

    -
    -
    Args

    fixed_image (sitk.Image) : the fixed image -moving_image (sitk.Image): the moving image, transformed to match fixed_image -options (dict) : registration options -structure (bool) : True if the image is a structure image

    -
    -
    Returns

    registered_image (sitk.Image): the rigidly registered moving image -transform (transform : the transform, can be used directly with

    +
    +
    Parameters
    +
      +
    • fixed_image ([SimpleITK.Image]) – The fixed (target/primary) image.

    • +
    • moving_image ([SimpleITK.Image]) – The moving (secondary) image.

    • +
    • fixed_structure (bool, optional) – If defined, a binary SimpleITK.Image used to mask metric +evaluation for the moving image. Defaults to False.

    • +
    • moving_structure (bool, optional) – If defined, a binary SimpleITK.Image used to mask metric +evaluation for the fixed image. Defaults to False.

    • +
    • resolution_staging (list, optional) – The multi-resolution downsampling factors. +Defaults to [8, 4, 2].

    • +
    • smooth_sigmas (list, optional) – The multi-resolution smoothing kernel scale (Gaussian). +Defaults to [4, 2, 1].

    • +
    • sampling_rate (float, optional) – The fraction of voxels sampled during each iteration. +Defaults to 0.1.

    • +
    • optimiser (str, optional) –

      The optimiser algorithm used for image registration. +Available options:

      -

      sitk.ResampleImageFilter

      +
        +
      • LBFSGS +(limited-memory Broyden–Fletcher–Goldfarb–Shanno (bounded).)

      • +
      • LBFSG +(limited-memory Broyden–Fletcher–Goldfarb–Shanno +(unbounded).)

      • +
      • CGLS (conjugate gradient line search)

      • +
      • gradient_descent

      • +
      • gradient_descent_line_search

      • +
      +
      +

      Defaults to “LBFGS”.

      +

    • +
    • metric (str, optional) –

      The metric to be optimised during image registration. +Available options:

      +
      +
        +
      • correlation

      • +
      • mean_squares

      • +
      • demons

      • +
      • mutual_information +(used with parameter number_of_histogram_bins_mi)

      • +
      +

      Defaults to “mean_squares”.

      +

    • +
    • initial_grid_spacing (int, optional) – Grid spacing of lower resolution stage (in mm). +Defaults to 64.

    • +
    • grid_scale_factors (list, optional) – Factors to determine grid spacing at each +multiresolution stage. +Defaults to [1, 2, 4].

    • +
    • interp_order (int, optional) – Interpolation order of final resampling. +Defaults to 3 (cubic).

    • +
    • default_value (int, optional) – Default image value. Defaults to -1000.

    • +
    • number_of_iterations (int, optional) – Number of iterations at each resolution stage. +Defaults to 20.

    • +
    • isotropic_resample (bool, optional) – Flag whether to resample to isotropic resampling +prior to registration. +Defaults to False.

    • +
    • initial_isotropic_size (int, optional) – Voxel size (in mm) of resampled isotropic image +(if used). Defaults to 1.

    • +
    • number_of_histogram_bins_mi (int, optional) – Number of histogram bins used when calculating +mutual information. Defaults to 30.

    • +
    • verbose (bool, optional) – Print image registration process information. Defaults to False.

    • +
    • ncores (int, optional) – Number of CPU cores used. Defaults to 8.

    • +
    +
    +
    Returns
    +

    The registered moving (secondary) image. +[SimleITK.Transform]: The linear transformation.

    +
    +
    Return type
    +

    [SimpleITK.Image]

    Notes

    • -
      smooth_sigmas are relative to resolution staging

      e.g. for image spacing of 1x1x1 mm^3, with smooth sigma=2 and resolution_staging=4, the scale of the Gaussian filter would be 2x4 = 8mm (i.e. 8x8x8 mm^3)

      +
      smooth_sigmas are relative to resolution staging

      e.g. for image spacing of 1x1x1 mm^3, with smooth sigma=2 and resolution_staging=4, the +scale of the Gaussian filter would be 2x4 = 8mm (i.e. 8x8x8 mm^3)

    - -
    -
    -platipy.imaging.registration.registration.control_point_spacing_distance_to_number(image, grid_spacing)
    -

    Convert grid spacing specified in distance to number of control points

    -
    - -
    -
    -platipy.imaging.registration.registration.convert_mask_to_distance_map(mask, squaredDistance=False, normalise=False)
    -
    - -
    -
    -platipy.imaging.registration.registration.convert_mask_to_reg_structure(mask, expansion=1, scale=<function <lambda>>)
    -
    - -
    -
    -platipy.imaging.registration.registration.deformable_registration_command_iteration(method)
    -

    Utility function to print information during demons registration

    -
    -
    -
    -platipy.imaging.registration.registration.fast_symmetric_forces_demons_registration(fixed_image, moving_image, resolution_staging=[8, 4, 1], iteration_staging=[10, 10, 10], isotropic_resample=False, initial_displacement_field=None, smoothing_sigma_factor=1, smoothing_sigmas=False, default_value=- 1024, ncores=1, structure=False, interp_order=2, trace=False, return_field=False)
    +
    +platipy.imaging.registration.deformable.fast_symmetric_forces_demons_registration(fixed_image, moving_image, resolution_staging=[8, 4, 1], iteration_staging=[10, 10, 10], isotropic_resample=False, initial_displacement_field=None, smoothing_sigma_factor=1, smoothing_sigmas=False, default_value=- 1000, ncores=1, interp_order=2, verbose=False)

    Deformable image propagation using Fast Symmetric-Forces Demons

    Args

    fixed_image (sitk.Image) : the fixed image @@ -236,11 +244,11 @@

    Submodules -
    -platipy.imaging.registration.registration.initial_registration(fixed_image, moving_image, moving_structure=False, fixed_structure=False, options={'final_interp': 3, 'metric': 'mean_squares', 'number_of_iterations': 50, 'optimiser': 'gradient_descent', 'sampling_rate': 0.1, 'shrink_factors': [8, 2, 1], 'smooth_sigmas': [4, 2, 0]}, default_value=- 1024, trace=False, reg_method='Similarity')
    -

    Rigid image registration using ITK

    -
    -
    Args

    fixed_image (sitk.Image) : the fixed image -moving_image (sitk.Image): the moving image, transformed to match fixed_image -options (dict) : registration options -structure (bool) : True if the image is a structure image

    -
    -
    Returns

    registered_image (sitk.Image): the rigidly registered moving image -transform (transform : the transform, can be used directly with

    -
    -

    sitk.ResampleImageFilter

    -
    -
    -
    -

    - -
    -
    -platipy.imaging.registration.registration.initial_registration_command_iteration(method)
    -

    Utility function to print information during initial (rigid, similarity, affine, translation) registration

    -
    -
    -
    -platipy.imaging.registration.registration.multiscale_demons(registration_algorithm, fixed_image, moving_image, initial_transform=None, initial_displacement_field=None, shrink_factors=None, smoothing_sigmas=None, iteration_staging=None, isotropic_resample=False, return_field=False)
    +
    +platipy.imaging.registration.deformable.multiscale_demons(registration_algorithm, fixed_image, moving_image, initial_transform=None, initial_displacement_field=None, isotropic_resample=None, resolution_staging=None, smoothing_sigmas=None, iteration_staging=None)

    Run the given registration algorithm in a multiscale fashion. The original scale should not be given as input as the original images are implicitly incorporated as the base of the pyramid. :param registration_algorithm: Any registration algorithm that has an Execute(fixed_image,

    @@ -311,123 +293,348 @@

    Submodules +

    platipy.imaging.registration.linear module

    -
    -platipy.imaging.registration.registration.smooth_and_resample(image, shrink_factor, smoothing_sigma, isotropic_resample=False, resampler=2)
    -
    +
    +platipy.imaging.registration.linear.alignment_registration(fixed_image, moving_image, moments=True)
    +

    A simple registration procedure that can align images in a single step. +Uses the image centres-of-mass (and optionally second moments) to +estimate the shift (and rotation) needed for alignment.

    +
    Parameters
      -
    • image – The image we want to resample.

    • -
    • shrink_factor – A number greater than one, such that the new image’s size is -original_size/shrink_factor. -If isotropic_resample is True, this will instead define the voxel size (mm)

    • -
    • smoothing_sigma – Sigma for Gaussian smoothing, this is in physical (image spacing) units, -not pixels.

    • -
    • isotropic_resample – A flag that changes the behaviour to resample the image to isotropic voxels of size (shrink_factor)

    • +
    • fixed_image ([SimpleITK.Image]) – The fixed (target/primary) image.

    • +
    • moving_image ([SimpleITK.Image]) – The moving (secondary) image.

    • +
    • moments (bool, optional) – Option to align images using the second moment. Defaults to True.

    Returns
    -

    Image which is a result of smoothing the input and then resampling it using the given sigma -and shrink factor.

    +

    The registered moving (secondary) image. +[SimleITK.Transform]: The linear transformation.

    +
    +
    Return type
    +

    [SimpleITK.Image]

    -
    -
    -platipy.imaging.registration.registration.stage_iteration(method)
    -

    Utility function to print information during stage change in registration

    +
    +platipy.imaging.registration.linear.linear_registration(fixed_image, moving_image, fixed_structure=None, moving_structure=None, reg_method='similarity', metric='mean_squares', optimiser='gradient_descent', shrink_factors=[8, 2, 1], smooth_sigmas=[4, 2, 0], sampling_rate=0.25, final_interp=2, number_of_iterations=50, default_value=- 1000, verbose=False)
    +

    Initial linear registration between two images. +The images are not required to be in the same space. +There are several transforms available, with options for the metric and optimiser to be used. +Note the default_value, which should be set to match the image modality.

    +
    +
    Parameters
    +
      +
    • fixed_image ([SimpleITK.Image]) – The fixed (target/primary) image.

    • +
    • moving_image ([SimpleITK.Image]) – The moving (secondary) image.

    • +
    • fixed_structure (bool, optional) – If defined, a binary SimpleITK.Image used to mask metric +evaluation for the moving image. Defaults to False.

    • +
    • moving_structure (bool, optional) – If defined, a binary SimpleITK.Image used to mask metric +evaluation for the fixed image. Defaults to False.

    • +
    • reg_method (str, optional) –

      The linear transformtation model to be used for image +registration. +Available options:

      +
      +
        +
      • translation

      • +
      • rigid

      • +
      • similarity

      • +
      • affine

      • +
      • scaleversor

      • +
      • scaleskewversor

      • +
      +
      +

      Defaults to “Similarity”.

      +

    • +
    • metric (str, optional) –

      The metric to be optimised during image registration. +Available options:

      +
      +
        +
      • correlation

      • +
      • mean_squares

      • +
      • mattes_mi

      • +
      • joint_hist_mi

      • +
      +
      +

      Defaults to “mean_squares”.

      +

    • +
    • optimiser (str, optional) –

      The optimiser algorithm used for image registration. +Available options:

      +
      +
        +
      • lbfgsb +(limited-memory Broyden–Fletcher–Goldfarb–Shanno (bounded).)

      • +
      • gradient_descent

      • +
      • gradient_descent_line_search

      • +
      +
      +

      Defaults to “gradient_descent”.

      +

    • +
    • shrink_factors (list, optional) – The multi-resolution downsampling factors. +Defaults to [8, 2, 1].

    • +
    • smooth_sigmas (list, optional) – The multi-resolution smoothing kernel scale (Gaussian). +Defaults to [4, 2, 0].

    • +
    • sampling_rate (float, optional) – The fraction of voxels sampled during each iteration. +Defaults to 0.25.

    • +
    • ants_radius (int, optional) – Used is the metric is set as “ants_radius”. Defaults to 3.

    • +
    • final_interp (int, optional) – The final interpolation order. Defaults to 2 (linear).

    • +
    • number_of_iterations (int, optional) – Number of iterations in each multi-resolution step. +Defaults to 50.

    • +
    • default_value (int, optional) – Default voxel value. Defaults to -1000.

    • +
    • verbose (bool, optional) – Print image registration process information. Defaults to False.

    • +
    +
    +
    Returns
    +

    The registered moving (secondary) image. +[SimleITK.Transform]: The linear transformation.

    +
    +
    Return type
    +

    [SimpleITK.Image]

    +
    +
    - +

    +
    +

    platipy.imaging.registration.utils module

    -
    -platipy.imaging.registration.registration.transform_propagation(fixed_image, moving_image, transform, structure=False, default_value=- 1024, interp=1, debug=False)
    -

    Transform propagation using ITK

    +
    +platipy.imaging.registration.utils.apply_deformable_transform(input_image, transform, is_structure=False, default_value=0, interpolator=1)
    +

    Helper function for applying deformable transforms.

    -
    Args

    fixed_image (sitk.Image) : the fixed image -moving_image (sitk.Image) : the moving image, to be propagated -transform (sitk.transform) : the transformation; e.g. VersorRigid3DTransform,

    +
    Args

    input_image (SimpleITK.Image): The image, to which the transform is applied +reference_image (SimpleITK.Image): The image will be resampled into this reference space. +transform (SimpleITK.Transform): The transformation +is_structure (bool): Will set appropriate parameters for transforming labels/structures. +default_value: Default (background) value. Defaults to 0. +interpolator (int, optional): The interpolation order.

    -

    AffineTransform

    +
    +
    Available options:
      +
    • SimpleITK.sitkNearestNeighbor

    • +
    • SimpleITK.sitkLinear

    • +
    • SimpleITK.sitkBSpline

    • +
    +
    +
    +

    Defaults to SimpleITK.sitkNearestNeighbor

    -

    structure (bool) : True if the image is a structure image -interp (int) : the interpolation

    +
    +
    Returns

    (SimpleITK.Image): the transformed image

    +
    +
    +
    +
    +
    +platipy.imaging.registration.utils.apply_linear_transform(input_image, reference_image, transform, is_structure=False, default_value=0, interpolator=1)
    +

    Helper function for applying linear transforms.

    +
    +
    Args

    input_image (SimpleITK.Image): The image, to which the transform is applied +reference_image (SimpleITK.Image): The image will be resampled into this reference space. +transform (SimpleITK.Transform): The transformation +is_structure (bool): Will set appropriate parameters for transforming labels/structures. +default_value: Default (background) value. Defaults to 0. +interpolator (int, optional): The interpolation order.

    -

    sitk.sitkNearestNeighbor -sitk.sitkLinear -sitk.sitkBSpline

    +
    +
    Available options:
      +
    • SimpleITK.sitkNearestNeighbor

    • +
    • SimpleITK.sitkLinear

    • +
    • SimpleITK.sitkBSpline

    • +
    +
    +
    +

    Defaults to SimpleITK.sitkNearestNeighbor

    -
    Returns

    registered_image (sitk.Image) : the rigidly registered moving image

    +
    Returns

    (SimpleITK.Image): the transformed image

    - -
    -
    +
    +
    +platipy.imaging.registration.utils.apply_transform(input_image, reference_image=None, transform=None, default_value=0, interpolator=1)
    +

    Transform a volume of structure with the given deformation field.

    +
    +
    Args

    input_image (SimpleITK.Image): The image, to which the transform is applied +reference_image (SimpleITK.Image): The image will be resampled into this reference space. +transform (SimpleITK.Transform): The transformation +default_value: Default (background) value. Defaults to 0. +interpolator (int, optional): The interpolation order.

    +
    +
    +
    Available options:
      +
    • SimpleITK.sitkNearestNeighbor

    • +
    • SimpleITK.sitkLinear

    • +
    • SimpleITK.sitkBSpline

    • +
    +
    +
    +

    Defaults to SimpleITK.sitkNearestNeighbor

    +
    +
    +
    Returns

    (SimpleITK.Image): the transformed image

    +
    +
    +
    +
    +
    +platipy.imaging.registration.utils.control_point_spacing_distance_to_number(image, grid_spacing)
    +

    Convert grid spacing specified in distance to number of control points

    +
    +
    +
    +platipy.imaging.registration.utils.convert_mask_to_distance_map(mask, squared_distance=False, normalise=False)
    +

    Generate a distance map from a binary label.

    +
    +
    Parameters
    +
      +
    • mask ([SimpleITK.Image]) – A binary label.

    • +
    • squared_distance (bool, optional) – Option to use the squared distance. Defaults to False.

    • +
    • normalise (bool, optional) – Normalise output to [0,1]. Defaults to False.

    • +
    +
    +
    Returns
    +

    The distance map as an image.

    +
    +
    Return type
    +

    [SimpleITK.Image]

    +
    +
    +
    +
    +
    +platipy.imaging.registration.utils.convert_mask_to_reg_structure(mask, expansion=1, scale=<function <lambda>>)
    +

    Generate a mask-like image to make structure-guided registration more +realistic via internal deformation within a binary mask.

    +
    +
    Parameters
    +
      +
    • mask ([SimpleITK.Image]) – The binary label.

    • +
    • expansion (int, optional) – For improved smoothness on the surface +(particularly complex structures) it will +often help to use some binary dilation. This +parameter defines the expansion in mm. +Defaults to 1.

    • +
    • scale ([function], optional) – Defines scaling to the distance map. +For example: lambda x:sitk.Log(x). +Defaults to lambda x:x.

    • +
    +
    +
    Returns
    +

    [description]

    +
    +
    Return type
    +

    [SimpleITK.Image]

    +
    +
    +
    +
    +
    +platipy.imaging.registration.utils.deformable_registration_command_iteration(method)
    +

    Utility function to print information during demons registration

    +
    +
    +
    +platipy.imaging.registration.utils.registration_command_iteration(method)
    +

    Utility function to print information during (rigid, similarity, translation, B-splines) +registration

    +
    +
    +
    +platipy.imaging.registration.utils.smooth_and_resample(image, isotropic_voxel_size_mm=None, shrink_factor=None, smoothing_sigma=None, interpolator=2)
    +
    +
    Parameters
    +
      +
    • image (SimpleITK.Image) – The image we want to resample.

    • +
    • isotropic_voxel_size_mm (float | None) – New voxel size in millimetres

    • +
    • shrink_factor (list | float) – A number greater than one, such that the new image’s size is +original_size/shrink_factor. Can also be specified independently for each +dimension (sagittal, coronal, axial).

    • +
    • smoothing_sigma (list | float) – Scale for Gaussian smoothing, this is in physical +(image spacing) units, not pixels. Can also be specified independently for +each dimension (sagittal, coronal, axial).

    • +
    +
    +
    Returns
    +

    Image which is a result of smoothing the input and then resampling +it using the specified Gaussian kernel and shrink factor.

    +
    +
    Return type
    +

    SimpleITK.Image

    +
    +
    +
    +
    +
    +platipy.imaging.registration.utils.stage_iteration(method)
    +

    Utility function to print information during stage change in registration

    +
    + +

    Module contents

    -
    -
    - - - - -
    - - -
    - -
    -

    - © Copyright 2020, Phillip Chlap & Rob Finnegan. + + -

    -
    + +
    + + -
    + + +
    + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.imaging.tests.html b/docs/site/gen/platipy.imaging.tests.html index 8d1e8058..7ee2ef24 100644 --- a/docs/site/gen/platipy.imaging.tests.html +++ b/docs/site/gen/platipy.imaging.tests.html @@ -1,354 +1,309 @@ + + + + + + + + + platipy.imaging.tests package - PlatiPy documentation + + + + - - - - - - - - - platipy.imaging.tests package — PlatiPy documentation - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
    +
    + +
    + +
    + +
    +
    + +
    +
    +
    + +

    platipy.imaging.tests package

    -
    +

    Submodules

    -
    -
    -

    platipy.imaging.tests.pull_data module

    -
    -
    -platipy.imaging.tests.pull_data.fetch_data(collection, patient_ids=None, output_directory=None, save_nifti=True, save_dicom=False)
    -

    Fetches data from TCIA from the dataset specified

    -
    -
    Parameters
    -
      -
    • collection (str) – The TCIA collection to fetch from

    • -
    • patient_ids (list, optional) – The patient IDs to fetch. If not set all patients are -fetched.

    • -
    • output_directory (str) – The directory in which to place fetched data

    • -
    • save_nifti (bool) – Whether or not to save the data as Nifti in the output directory

    • -
    • save_dicom (bool) – Whether or not to save the data as Dicom in the output directory

    • -
    -
    -
    Returns
    -

    The patients and directories where their data was fetched

    -
    -
    Return type
    -

    dict

    -
    -
    -
    - +
    +
    +

    platipy.imaging.tests.data module

    -
    -platipy.imaging.tests.pull_data.get_hn_data(number_of_patients=3)
    -

    Gets some images and structures from the HN (Lung) dataset in TCIA.

    -
    -
    Keyword Arguments
    -

    {int} -- The number of patients to fetch (default (number_of_patients) – {3})

    -
    -
    Returns
    -

    dict – dictionary of patient ids as key and path to data as value

    -
    -
    -
    - +
    +platipy.imaging.tests.data.download_and_extract_zip_file(zip_url, output_directory)
    +
    -
    -platipy.imaging.tests.pull_data.get_lung_data(number_of_patients=3)
    -

    Gets some images and structures from the Head-Neck-Radiomics-HN1 (HN) dataset in TCIA.

    -
    -
    Keyword Arguments
    -

    {int} -- The number of patients to fetch (default (number_of_patients) – {3})

    -
    -
    Returns
    -

    dict – dictionary of patient ids as key and path to data as value

    -
    -
    -
    - +
    +platipy.imaging.tests.data.get_lung_dicom(output_directory='./data/dicom')
    +
    -
    -platipy.imaging.tests.pull_data.get_patients_in_collection(collection)
    -

    Get a list of sorted patient IDs in a TCIA collection

    -
    -
    Parameters
    -

    collection (str) – TCIA collection

    -
    -
    Returns
    -

    List of sorted Patient IDs in the collection

    -
    -
    Return type
    -

    list

    -
    -
    -
    - -
    -
    +
    +platipy.imaging.tests.data.get_lung_nifti(output_directory='./data/nifti')
    +
    + +

    platipy.imaging.tests.test_bronchus module

    platipy.imaging.tests.test_bronchus.assert_bronchus_mask(bronchus_mask)

    Checks that the bronchus mask looks as expected for the tests in this file

    -
    platipy.imaging.tests.test_bronchus.assert_lung_mask(lung_mask)

    Checks that the lung mask looks as expected for the tests in this file

    -
    platipy.imaging.tests.test_bronchus.bronchus_data()

    Gets the data needed for the bronchus tests

    Returns
    -

    dict – Data for broncus test

    +

    Path to Data for bronchus test

    +
    +
    Return type
    +

    pathlib.Path

    -
    platipy.imaging.tests.test_bronchus.test_bronchus_segmentation(bronchus_data)

    Tests the bronchus segmentation algorithm

    -
    platipy.imaging.tests.test_bronchus.test_bronchus_service(bronchus_data)

    An end-to-end test to check that the bronchus service is running as expected

    -
    platipy.imaging.tests.test_bronchus.test_lung_segmentation(bronchus_data)

    Tests the lung segmentation used as an initial step of bronchus segmentation

    - -
    -
    + +

    platipy.imaging.tests.test_cardiac module

    platipy.imaging.tests.test_cardiac.cardiac_data()
    -

    Gets the data needed for the cardiac tests

    +

    Generates the data needed for the cardiac tests

    Returns

    dict – Data for cardiac test

    -
    platipy.imaging.tests.test_cardiac.test_cardiac_service(cardiac_data)

    An end-to-end test to check that the cardiac service is running as expected

    - -
    -
    +
    +
    +platipy.imaging.tests.test_cardiac.test_cardiac_structure_guided_service(cardiac_data)
    +

    An end-to-end test to check that the cardiac structure guided service is running as expected

    +
    + +
    +

    platipy.imaging.tests.test_visualiser module

    +
    +
    +platipy.imaging.tests.test_visualiser.nifti_data()
    +
    +
    +
    +platipy.imaging.tests.test_visualiser.test_animation_visualisation(nifti_data)
    +
    +
    +
    +platipy.imaging.tests.test_visualiser.test_comparison_overlay_visualisation(nifti_data)
    +
    +
    +
    +platipy.imaging.tests.test_visualiser.test_contour_visualisation(nifti_data)
    +
    +
    +
    +platipy.imaging.tests.test_visualiser.test_scalar_overlay_visualisation(nifti_data)
    +
    +
    +

    Module contents

    -
    -
    - + + - - -
    - - -
    - -
    -

    - © Copyright 2020, Phillip Chlap & Rob Finnegan. - -

    -
    + +
    + + -
    + + +
    + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.imaging.utils.html b/docs/site/gen/platipy.imaging.utils.html index af4ec613..381560e5 100644 --- a/docs/site/gen/platipy.imaging.utils.html +++ b/docs/site/gen/platipy.imaging.utils.html @@ -1,310 +1,377 @@ + + + + + + + + + platipy.imaging.utils package - PlatiPy documentation + + + + - - - - - - - - - platipy.imaging.utils package — PlatiPy documentation - - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
    +
    + +
    + +
    + +
    +
    + +
    +
    +
    + +

    platipy.imaging.utils package

    -
    +

    Submodules

    -
    -
    -

    platipy.imaging.utils.tools module

    +
    +
    +

    platipy.imaging.utils.crop module

    Various useful utility functions for atlas based segmentation algorithms.

    -
    -platipy.imaging.utils.tools.crop_to_roi(image, size, index)
    -
    - +
    +platipy.imaging.utils.crop.crop_to_roi(image, size, index)
    +

    Utility function for cropping images

    +
    +
    +
    +platipy.imaging.utils.crop.label_to_roi(label, expansion_mm=[0, 0, 0], return_as_list=False)
    +

    Generates a region of interest (ROI), defined by a starting index (z,y,x) +and size (s_z, s_y, s_x). This can be used to crop images/labels.

    +
    +
    Parameters
    +
      +
    • label (sitk.Image | list) – Binary label image/mask/structure to define to ROI

    • +
    • expansion_mm (list, optional) – An optional expansion of the box (in each direciton). +Defaults to [0, 0, 0].

    • +
    +
    +
    Returns
    +

    crop_box_size, crop_box_starting_index

    +
    +
    Return type
    +

    tuple

    +
    +
    +
    +
    +
    +

    platipy.imaging.utils.lung module

    -
    -platipy.imaging.utils.tools.detect_holes(img, lower_threshold=- 10000, upper_threshold=- 400)
    +
    +platipy.imaging.utils.lung.detect_holes(img, lower_threshold=- 10000, upper_threshold=- 400)

    Detect all (air) holes in given image. Default threshold values given are defined for CT.

    -
    Returns
    -

    image containing labels of all holes detected +

    Parameters
    +
      +
    • img (sitk.Image) – The image in which to detect holes.

    • +
    • lower_threshold (int, optional) – The lower threshold of intensities. Defaults to -10000.

    • +
    • upper_threshold (int, optional) – The upper threshold of intensities. Defaults to -400.

    • +
    +
    +
    Returns
    +

    image containing labels of all holes detected labels: a list of holes detected and some of their properties

    -
    Return type
    -

    label_image

    +
    Return type
    +

    label_image

    -
    -
    -platipy.imaging.utils.tools.fill_holes(img, label_image, labels, external_mask, lung_mask)
    -
    - +
    +platipy.imaging.utils.lung.fill_holes(img, label_image, external_mask, lung_mask, fill_value=50)
    +

    Returns the input image with all holes filled (except for the external and lung holes).

    +
    +
    Parameters
    +
      +
    • img (sitk.Image) – The image to fill

    • +
    • label_image (sitk.Image) – Label image from detect_holes

    • +
    • external_mask (sitk.Image) – The external mask of the patient.

    • +
    • lung_mask (sitk.Image) – The lung mask of the patient.

    • +
    • fill_value (int) – The value to use to fill the holes. Defaults to 50.

    • +
    +
    +
    Returns
    +

    The image with holes filled

    +
    +
    Return type
    +

    sitk.Image

    +
    +
    +
    -
    -platipy.imaging.utils.tools.gaussian_curve(x, a, m, s)
    -
    - +
    +platipy.imaging.utils.lung.get_external_mask(label_image, labels, kernel_radius=5)
    +

    Get a External mask given a label image and labels computed with detect_holes

    +
    +
    Parameters
    +
      +
    • label_image (sitk.Image) – Label image from detect_holes

    • +
    • labels (list) – List of labels from detect_holes

    • +
    • kernel_radius (int, optional) – The width of the radius used by binary close. Defaults to 5.

    • +
    +
    +
    Returns
    +

    External mask

    +
    +
    Return type
    +

    sitk.Image

    +
    +
    +
    -
    -platipy.imaging.utils.tools.get_com(label, as_int=True)
    -
    - +
    +platipy.imaging.utils.lung.get_lung_mask(label_image, labels, kernel_radius=2)
    +

    Get a Lung mask given a label image and labels computed with detect_holes

    +
    +
    Parameters
    +
      +
    • label_image (sitk.Image) – Label image from detect_holes

    • +
    • labels (list) – List of labels from detect_holes

    • +
    • kernel_radius (int, optional) – The width of the radius used by binary close. Defaults to 2.

    • +
    +
    +
    Returns
    +

    Lung mask

    +
    +
    Return type
    +

    sitk.Image

    +
    +
    +
    +
    +
    +

    platipy.imaging.utils.valve module

    -
    -platipy.imaging.utils.tools.get_crop_bounding_box(img, mask)
    +
    +platipy.imaging.utils.valve.generate_valves_from_chambers(img_chamber_1, img_chamber_2, radius_mm=10)
    - -
    -
    -platipy.imaging.utils.tools.get_external_mask(label_image, labels, kernel_radius=5)
    -

    Gets the external mask based on the label image and labels generated using detect_holes

    -
    -
    -
    -platipy.imaging.utils.tools.get_lung_mask(label_image, labels, kernel_radius=2)
    +
    +platipy.imaging.utils.valve.generate_valves_from_vessels(vessel, thickness_mm=4, erosion_mm=2)
    - +
    +
    +

    platipy.imaging.utils.vessel module

    -
    -platipy.imaging.utils.tools.itk_to_sitk(itk_image)
    -

    Helper function to convert ITK images to SimpleITK images

    +
    +platipy.imaging.utils.vessel.com_from_image_list(sitk_image_list, condition_type='count', condition_value=0, scan_direction='z')
    +
    +
    Input: list of SimpleITK images

    minimum total slice area required for the tube to be inserted at that slice +scan direction: x = sagittal, y=coronal, z=axial

    +
    +
    +

    Output: mean centre of mass positions, with shape (NumSlices, 2) +Note: positions are converted into image space by default

    -
    -
    -platipy.imaging.utils.tools.label_to_roi(image, label_list, expansion=[0, 0, 0])
    +
    +platipy.imaging.utils.vessel.convert_simpleitk_to_vtk(img)
    -
    -
    -platipy.imaging.utils.tools.median_absolute_deviation(data, axis=None)
    -

    Median Absolute Deviation: a “Robust” version of standard deviation. -Indices variabililty of the sample. -https://en.wikipedia.org/wiki/Median_absolute_deviation

    +
    +platipy.imaging.utils.vessel.simpleitk_image_from_vtk_tube(tube, sitk_reference_image)
    +

    Input: VTK tube, referenceImage (used for spacing, etc.) +Output: SimpleITK image +Note: Uses binary output (background 0, foreground 1)

    -
    -
    -platipy.imaging.utils.tools.sitk_to_itk(sitk_image, copy_info=True)
    -

    Helper function to convert SimpleITK images to ITK images

    +
    +platipy.imaging.utils.vessel.tube_from_com_list(com_list, radius)
    +

    Input: image-space positions along the tube centreline. +Output: VTK tube +Note: positions do not have to be continuous - the tube is interpolated in real space

    -
    -
    -platipy.imaging.utils.tools.vectorised_transform_index_to_physical_point(image, point_array, rotate=True)
    -

    Transforms a set of points from array indices to real-space

    -
    - +
    +platipy.imaging.utils.vessel.vessel_spline_generation(reference_image, atlas_set, vessel_name_list, vessel_radius_mm_dict, stop_condition_type_dict, stop_condition_value_dict, scan_direction_dict, atlas_label='DIR')
    +
    -
    -platipy.imaging.utils.tools.vectorised_transform_physical_point_to_index(image, point_array, rotate=True)
    -

    Transforms a set of points from real-space to array indices

    +
    +platipy.imaging.utils.vessel.write_vtk_tube_to_file(tube, filename)
    +

    Input: VTK tube +Output: exit success +Note: format is XML VTP

    - -
    -
    + +

    Module contents

    -
    -
    - - - - -
    - - -
    + + -
    -

    - © Copyright 2020, Phillip Chlap & Rob Finnegan. - -

    -
    + +
    + + -
    + + +
    + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/gen/platipy.imaging.visualisation.html b/docs/site/gen/platipy.imaging.visualisation.html index c40145ec..91549272 100644 --- a/docs/site/gen/platipy.imaging.visualisation.html +++ b/docs/site/gen/platipy.imaging.visualisation.html @@ -1,347 +1,269 @@ + + + + + + + + + platipy.imaging.visualisation package - PlatiPy documentation + + + + - - - - - - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
    +
    + +
    + +
    + +
    +
    + +
    +
    +
    + +

    platipy.imaging.visualisation package

    -
    +

    Submodules

    -
    -
    -

    platipy.imaging.visualisation.Python3DMesh module

    -
    -
    -

    platipy.imaging.visualisation.VesselSurfaces-Auto module

    -
    -
    -

    platipy.imaging.visualisation.crosshairs module

    -
    -
    -

    platipy.imaging.visualisation.surface_operations module

    -
    -
    -

    platipy.imaging.visualisation.tools module

    +
    +
    +

    platipy.imaging.visualisation.animation module

    -
    -class platipy.imaging.visualisation.tools.ImageVisualiser(image, cut=None, axis='ortho', window=[-250, 500], figure_size_in=10, limits=None, colormap=<matplotlib.colors.LinearSegmentedColormap object>)
    -

    Bases: object

    -

    Class to assist with visualising images and overlaying contours, scalars and bounding boxes.

    +
    +class platipy.imaging.visualisation.animation.FileWriter(*args, **kwargs)
    +

    Bases: matplotlib.animation.FileMovieWriter

    +

    Class to write image frames for animation

    -
    -add_bounding_box(bounding_box, name=None, color=None)
    -
    - +
    +finish()
    +

    Finish any processing for writing the movie.

    +
    -
    -add_comparison_overlay(image, name=None, color_rotation=0.35)
    -

    Overlay a comparison image on the existing image

    -
    -
    Parameters
    -
      -
    • sitk.Image) (image) – Image (will be displayed as a comparison).

    • -
    • name (str, optional) – Name to give the image. Defaults to None.

    • -
    • color_rotation (float, optional) – Defines the hue of the original image (0 - 0.5).

    • -
    -
    -
    Raises
    -

    ValueError – Comparison overlay must be of type sitk.Image.

    -
    -
    +
    +grab_frame(**savefig_kwargs)
    +

    Grab the image information from the figure and save as a movie frame.

    +

    All keyword arguments in savefig_kwargs are passed on to the +~.Figure.savefig call that saves the figure.

    -
    -
    -add_contour(contour, name=None, color=None, colorbase=<matplotlib.colors.LinearSegmentedColormap object>, linewidth=2)
    -

    Add a contour to overlay

    +
    +setup(fig, dpi, frame_prefix)
    +

    Setup for writing the movie file.

    Parameters
      -
    • contour (sitk.Image|dict) – Contour mask or dict containing contour masks.

    • -
    • name (str, optional) – Name to give the contour (only used if passing sitk.Image as -contour). Defaults to None.

    • -
    • color (str|tuple|list, optional) – The color to use when drawing the contour(s). -Defaults to None.

    • -
    -
    -
    Raises
    -
      -
    • ValueError – Contour must be dict of sitk.Image.

    • -
    • ValueError – If passing a dict for contour, all values must be sitk.Image.

    • +
    • fig (~matplotlib.figure.Figure) – The figure to grab the rendered frames from.

    • +
    • outfile (str) – The filename of the resulting movie file.

    • +
    • dpi (float, optional) – The dpi of the output file. This, with the figure size, +controls the size in pixels of the resulting movie file. +Default is fig.dpi.

    • +
    • frame_prefix (str, optional) – The filename prefix to use for temporary files. If None (the +default), files are written to a temporary directory which is +deleted by cleanup (regardless of the value of clear_temp).

    • +
    • clear_temp (bool, optional) – If the temporary files should be deleted after stitching +the final result. Setting this to False can be useful for +debugging. Defaults to True.

    - -
    -
    -add_scalar_overlay(scalar_image, name=None, colormap=<matplotlib.colors.LinearSegmentedColormap object>, alpha=0.75, min_value=0.1, max_value=False, discrete_levels=False, show_colorbar=True)
    -

    Overlay a scalar image on to the existing image

    +
    +
    +supported_formats = ['png']
    +
    +
    +
    +
    +platipy.imaging.visualisation.animation.generate_animation_from_image_sequence(image_list, output_file='animation.gif', fps=10, contour_list=None, scalar_list=None, figure_size_in=6, image_cmap=<matplotlib.colors.LinearSegmentedColormap object>, contour_cmap=<matplotlib.colors.LinearSegmentedColormap object>, scalar_cmap=<matplotlib.colors.ListedColormap object>, image_window=[-1000, 800], scalar_min=None, scalar_max=None, scalar_alpha=0.5, image_origin='lower')
    +

    Make a GIF animation from the list of images supplied

    Parameters
      -
    • sitk.Image|dict) (scalar_image) – Scalar image or dict containing scalar images.

    • -
    • name (str, optional) – Name to give the scalar image (only used if passing sitk.Image as -scalar image). Defaults to None.

    • -
    • colormap (matplotlib.colors.Colormap, optional) – The colormap to be used when -overlaying this scalar image. Defaults -to plt.cm.get_cmap(“Spectral”).

    • -
    • alpha (float, optional) – Alpha to apply to overlay. Defaults to 0.75.

    • -
    • min_value (float, optional) – Values below this value aren’t rendered. Defaults to 0.1.

    • +
    • image_list (list) – List of 2D sitk.Image’s to render

    • +
    • output_file (str, optional) – Path to output GIF file. Defaults to “animation.gif”.

    • +
    • fps (int, optional) – Frames per second. Defaults to 10.

    • +
    • contour_list (list, optional) – List of contours to overlay. Defaults to None.

    • +
    • scalar_list (list, optional) – List of scalars to overlay. Defaults to None.

    • +
    • figure_size_in (int, optional) – Size of figure. Defaults to 6.

    • +
    • image_cmap ([type], optional) – Colormap for image. Defaults to plt.cm.get_cmap(“Greys_r”).

    • +
    • contour_cmap ([type], optional) – Colormap for contours. Defaults to plt.cm.get_cmap(“jet”).

    • +
    • scalar_cmap ([type], optional) – Colormap for scalars. Defaults to plt.cm.get_cmap(“magma”).

    • +
    • image_window (list, optional) – Window for image. Defaults to [-1000, 800].

    • +
    • scalar_min (float, optional) – Minimum value for scalar. Defaults to None.

    • +
    • scalar_max (float, optional) – Maximum value for scalar. Defaults to None.

    • +
    • scalar_alpha (float, optional) – Alpha value for scalar. Defaults to 0.5.

    • +
    • image_origin (str, optional) – Origin of image. Defaults to “lower”.

    Raises
    -
      -
    • ValueError – Scalar overlay must be dict of sitk.Image.

    • -
    • ValueError – If passing a dict for contour, all values must be sitk.Image.

    • -
    +

    ValueError – Raised if image list does not contain sitk.Image’s

    -
    -
    - -
    -
    -add_vector_overlay(vector_image, name=None, colormap=<matplotlib.colors.LinearSegmentedColormap object>, alpha=0.75, arrow_scale=0.25, arrow_width=1, subsample=4, color_function='perpendicular')
    -

    Overlay a vector field on to the existing image

    -
    -
    Parameters
    -
      -
    • sitk.Image|dict) (vector_image) – Vector image (will be displayed as ).

    • -
    • name (str, optional) – Name to give the vector field (only used if passing -sitk.Image as vector field). Defaults to None.

    • -
    • colormap (matplotlib.colors.Colormap, optional) – The colormap to be used when -overlaying this vector field. Defaults -to plt.cm.get_cmap(“Spectral”).

    • -
    • alpha (float, optional) – Alpha to apply to overlay vectors. Defaults to 0.75.

    • -
    • arrow_scale (float, optional) – Relative scaling of vectors. Defaults to 0.25.

    • -
    • arrow_width (float, optional) – Width of vector field arrow. Defaults to 0.25.

    • -
    • subsample (int, optional) – Defines to subsampling ratio of displayed vectors. -Defaults to 4.

    • -
    • color_function (str, optional) – Determines how vectors are colored. Options: -‘perpendicular’ - vectors colored by perpendicular value -‘magnitude’ - vectors colored by magnitude.

    • -
    +
    Returns
    +

    The animation object

    -
    Raises
    -

    ValueError – Vector overlay must be of type sitk.Image.

    +
    Return type
    +

    matplotlib.animation.FuncAnimation

    - -
    -
    -adjust_view()
    -

    adjust_view is a helper function for modifying axis limits. -Specify limits when initialising the ImageVisulaiser to use.

    -
    - -
    -
    -clear()
    -

    Clear all overlays

    -
    - -
    -
    -display_slice()
    -

    Display the configured image slice

    -
    - -
    -
    -property image
    -
    - -
    -
    -property labelmap
    -
    - -
    -
    -overlay_bounding_boxes(color='r')
    -

    Overlay bounding boxes onto existing figure

    -
    -
    Parameters
    -

    color (str|list|tuple, optional) – Color of bounding box. Defaults to “r”.

    -
    -
    +
    +
    +

    platipy.imaging.visualisation.utils module

    +
    +
    +class platipy.imaging.visualisation.utils.VisualiseBoundingBox(bounding_box, name, color='r', linewidth=2)
    +

    Bases: object

    +

    Class to represent the visualiation of a bounding box

    - -
    -
    -overlay_comparison()
    -

    Display an overlay comparison

    -
    -
    Parameters
    -

    color_rotation (float, optional) – The hue used to color the original image (0 - 0.5).

    -
    -
    +
    +
    +class platipy.imaging.visualisation.utils.VisualiseComparisonOverlay(image, name, color_rotation=0.35)
    +

    Bases: object

    +

    Class to represent the visualiation of a comparison image

    - -
    -
    -overlay_contours()
    -

    Overlay the contours on to the current figure image

    +
    +
    +class platipy.imaging.visualisation.utils.VisualiseContour(image, name, color=None, linewidth=2)
    +

    Bases: object

    +

    Class to represent the visualiation of a contour

    - -
    -
    -overlay_scalar_field()
    -

    Overlay the scalar image onto the existing figure

    +
    +
    +class platipy.imaging.visualisation.utils.VisualiseScalarOverlay(image, name, colormap=<matplotlib.colors.LinearSegmentedColormap object>, alpha=0.75, min_value=False, max_value=False, discrete_levels=False, mid_ticks=False, show_colorbar=True, norm=None)
    +

    Bases: object

    +

    Class to represent the visualiation of a scalar overlay

    - -
    -
    -overlay_vector_field()
    -

    Overlay vector field onto existing figure

    +
    +
    +class platipy.imaging.visualisation.utils.VisualiseVectorOverlay(image, name, colormap=<matplotlib.colors.LinearSegmentedColormap object>, alpha=0.75, arrow_scale=0.25, arrow_width=1, subsample=4, color_function='perpendicular', invert_field=True, show_colorbar=True)
    +

    Bases: object

    +

    Class to represent the visualiation of a vector overlay

    - -
    -
    -reorientate_vector_field(axis, vector_ax, vector_cor, vector_sag, invert_field=True)
    +
    +
    +platipy.imaging.visualisation.utils.generate_comparison_colormix(image_list, arr_slice=None, window=- 250, 500, color_rotation=0.35)
    +
    +
    +
    +platipy.imaging.visualisation.utils.project_onto_arbitrary_plane(image, projection_name='mean', projection_axis=0, rotation_axis=[1, 0, 0], rotation_angle=0, default_value=- 1000, resample_interpolation=2)
    +
    +
    +
    +platipy.imaging.visualisation.utils.reorientate_vector_field(axis, vector_ax, vector_cor, vector_sag, invert_field=True)

    Reorients vector field components for rendering This is necessary after converting from sitk.Image to np.array

    @@ -361,10 +283,9 @@

    platipy.imaging.visualisation.surface_operations module

    - -
    -
    -return_slice(axis, index)
    +
    +
    +platipy.imaging.visualisation.utils.return_slice(axis, index)

    Prepares a slice tuple to use for extracting a slice for rendering

    Parameters
    @@ -381,21 +302,9 @@

    platipy.imaging.visualisation.surface_operations module

    - -
    -
    -set_limits_from_label(label, expansion=[0, 0, 0], min_value=0)
    -
    - -
    -
    -show()
    -

    Render the image with all overlays

    -
    - -
    -
    -subsample_vector_field(axis, cut, subsample=1)
    +
    +
    +platipy.imaging.visualisation.utils.subsample_vector_field(axis, cut, subsample=1)

    Prepares a slice tuple to use for extracting a slice for rendering

    Parameters
    @@ -413,10 +322,9 @@

    platipy.imaging.visualisation.surface_operations module

    - -
    -
    -vector_image_grid(axis, vector_field_array, subsample=1)
    +
    +
    +platipy.imaging.visualisation.utils.vector_image_grid(axis, vector_field_array, subsample=1)

    Prepares a grid for rendering a vector field on an image

    Parameters
    @@ -434,200 +342,229 @@

    platipy.imaging.visualisation.surface_operations module

    - -
    - +
    +
    +

    platipy.imaging.visualisation.visualiser module

    -
    -class platipy.imaging.visualisation.tools.VisualiseBoundingBox(bounding_box, name, color=None)
    +
    +class platipy.imaging.visualisation.visualiser.ImageVisualiser(image, cut=None, axis='ortho', window=[-250, 500], figure_size_in=10, limits=None, colormap=<matplotlib.colors.LinearSegmentedColormap object>, origin='normal', projection=False)

    Bases: object

    -

    Class to represent the visualiation of a bounding box

    +

    Class to assist with visualising images and overlaying contours, scalars and bounding +boxes.

    +
    +
    +add_bounding_box(bounding_box, name=None, color='r', linewidth=2)
    +

    Add a bounding box to draw.

    +
    +
    Parameters
    +
      +
    • bounding_box (dict|list|tuple|sitk.Image) – Dictionary of bounding boxes where list key +is the name of the bounding box. If list or tuple then pass +bounding box in format (x, y, z, w, h, d). If sitk.Image then +mask is assumed and bounding box around this mask is used.

    • +
    • name (list, optional) – Name of this bounding box if bounding_box is list, tuple or +sitk.Image. Defaults to None.

    • +
    • color (str|tuple|list, optional) – The color to use when drawing the bounding box(es). +Defaults to None.

    • +
    • linewidth (int, optional) – The line width of the bounding box(es). Defaults to 2.

    • +
    +
    +
    Raises
    +

    ValueError – Raised if input is not a list or tuple of length 6 or and sitk.Image.

    +
    +
    - -
    -
    -class platipy.imaging.visualisation.tools.VisualiseComparisonOverlay(image, name, color_rotation=0.35)
    -

    Bases: object

    -

    Class to represent the visualiation of a comparison image

    +
    +
    +add_comparison_overlay(image, name=None, color_rotation=0.35)
    +

    Overlay a comparison image on the existing image

    +
    +
    Parameters
    +
      +
    • sitk.Image) (image) – Image (will be displayed as a comparison).

    • +
    • name (str, optional) – Name to give the image. Defaults to None.

    • +
    • color_rotation (float, optional) – Defines the hue of the original image (0 - 0.5).

    • +
    +
    +
    Raises
    +

    ValueError – Comparison overlay must be of type sitk.Image.

    +
    +
    - -
    -
    -class platipy.imaging.visualisation.tools.VisualiseContour(image, name, color=None, linewidth=2)
    -

    Bases: object

    -

    Class to represent the visualiation of a contour

    +
    +
    +add_contour(contour, name=None, color=None, colormap=<matplotlib.colors.LinearSegmentedColormap object>, linewidth=2, show_legend=True)
    +

    Add a contour as overlay

    +
    +
    Parameters
    +
      +
    • contour (sitk.Image|dict) – Contour mask or dict containing contour masks.

    • +
    • name (str, optional) – Name to give the contour (only used if passing sitk.Image as +contour). Defaults to None.

    • +
    • color (str|tuple|list, optional) – The color to use when drawing the contour(s). +Defaults to None.

    • +
    +
    +
    Raises
    +
      +
    • ValueError – Contour must be dict of sitk.Image.

    • +
    • ValueError – If passing a dict for contour, all values must be sitk.Image.

    • +
    +
    +
    - -
    -
    -class platipy.imaging.visualisation.tools.VisualiseScalarOverlay(image, name, colormap=<matplotlib.colors.LinearSegmentedColormap object>, alpha=0.75, min_value=0.1, max_value=False, discrete_levels=False, show_colorbar=True)
    -

    Bases: object

    -

    Class to represent the visualiation of a scalar overlay

    +
    +
    +add_scalar_overlay(scalar_image, name=None, colormap=<matplotlib.colors.LinearSegmentedColormap object>, alpha=0.75, min_value=False, max_value=False, discrete_levels=False, mid_ticks=False, show_colorbar=True, norm=None)
    +

    Overlay a scalar image on to the existing image

    +
    +
    Parameters
    +
      +
    • sitk.Image|dict) (scalar_image) – Scalar image or dict containing scalar images.

    • +
    • name (str, optional) – Name to give the scalar image (only used if passing sitk.Image as +scalar image). Defaults to None.

    • +
    • colormap (matplotlib.colors.Colormap, optional) – The colormap to be used when +overlaying this scalar image. Defaults +to plt.cm.get_cmap(“Spectral”).

    • +
    • alpha (float, optional) – Alpha to apply to overlay. Defaults to 0.75.

    • +
    • min_value (float, optional) – Values below this value aren’t rendered. Defaults to 0.1.

    • +
    +
    +
    Raises
    +
      +
    • ValueError – Scalar overlay must be dict of sitk.Image.

    • +
    • ValueError – If passing a dict for contour, all values must be sitk.Image.

    • +
    +
    +
    - -
    -
    -class platipy.imaging.visualisation.tools.VisualiseVectorOverlay(image, name, colormap=<matplotlib.colors.LinearSegmentedColormap object>, alpha=0.75, arrow_scale=0.25, arrow_width=1, subsample=4, color_function='perpendicular', invert_field=True)
    -

    Bases: object

    -

    Class to represent the visualiation of a vector overlay

    +
    +
    +add_vector_overlay(vector_image, name=None, colormap=<matplotlib.colors.LinearSegmentedColormap object>, alpha=0.75, arrow_scale=0.25, arrow_width=1, subsample=4, color_function='perpendicular', show_colorbar=True)
    +

    Overlay a vector field on to the existing image

    +
    +
    Parameters
    +
      +
    • sitk.Image|dict) (vector_image) – Vector image (will be displayed as ).

    • +
    • name (str, optional) – Name to give the vector field (only used if passing +sitk.Image as vector field). Defaults to None.

    • +
    • colormap (matplotlib.colors.Colormap, optional) – The colormap to be used when +overlaying this vector field. Defaults +to plt.cm.get_cmap(“Spectral”).

    • +
    • alpha (float, optional) – Alpha to apply to overlay vectors. Defaults to 0.75.

    • +
    • arrow_scale (float, optional) – Relative scaling of vectors. Defaults to 0.25.

    • +
    • arrow_width (float, optional) – Width of vector field arrow. Defaults to 0.25.

    • +
    • subsample (int, optional) – Defines to subsampling ratio of displayed vectors. +Defaults to 4.

    • +
    • color_function (str, optional) – Determines how vectors are colored. Options: +‘perpendicular’ - vectors colored by perpendicular +value +‘magnitude’ - vectors colored by magnitude.

    • +
    +
    +
    Raises
    +

    ValueError – Vector overlay must be of type sitk.Image.

    +
    +
    - -
    -
    -platipy.imaging.visualisation.tools.generate_animation_from_image_sequence(image_list, output_file='animation.gif', fps=10, contour_list=False, scalar_list=False, figure_size_in=6, image_cmap=<matplotlib.colors.LinearSegmentedColormap object>, contour_cmap=<matplotlib.colors.LinearSegmentedColormap object>, scalar_cmap=<matplotlib.colors.ListedColormap object>, image_window=[-1000, 800], scalar_min=False, scalar_max=False, scalar_alpha=0.5, image_origin='lower')
    -
    - -
    -
    -platipy.imaging.visualisation.tools.project_onto_arbitrary_plane(image, projection_name='mean', projection_axis=0, rotation_axis=[1, 0, 0], rotation_angle=0, default_value=- 1000, resample_interpolation=2)
    -
    - -
    -
    -

    platipy.imaging.visualisation.vis_tools module

    -

    Module name: Visualisation -Author: Robert Finnegan -Date: December 2018 -Description: -——————————— -- Medical Imaging -- AutoSegmentation Output: deformation fields, delineations -- Slice generation -———————————

    -
    -
    -platipy.imaging.visualisation.vis_tools.displayComparisonSlice(im_a, im_b, axis='ortho', cut=None, figSize=6, window=[- 250, 500], addCBar=False, colour_rotation=0.35, cmap_label='')
    -
    - -
    -
    -platipy.imaging.visualisation.vis_tools.displaySlice(im, axis='ortho', cut=None, figSize=6, cmap=<matplotlib.colors.LinearSegmentedColormap object>, window=[-250, 500], addCBar=False, cmap_label='')
    -
    - -
    -
    -platipy.imaging.visualisation.vis_tools.keepLargestConnectedComponent(binaryImage)
    -
    - -
    -
    -platipy.imaging.visualisation.vis_tools.main(arguments)
    -
    - -
    -
    -platipy.imaging.visualisation.vis_tools.overlayBox(box, fig, axis, color='r')
    -
    - -
    -
    -platipy.imaging.visualisation.vis_tools.overlayContour(contour_input, fig, axis, cut, use_legend=True, color_base=<matplotlib.colors.LinearSegmentedColormap object>)
    -
    - -
    -
    -platipy.imaging.visualisation.vis_tools.overlayScalarField(scalarIm, fig, axis, cut, color_base=<matplotlib.colors.LinearSegmentedColormap object>, addCBar=False, window=[0, 1], plotArgs={'alpha': 0.75, 'sMin': 0.01})
    -
    - -
    -
    -platipy.imaging.visualisation.vis_tools.probabilityThreshold(inputImage, lowerThreshold=0.5)
    -
    - -
    -
    -platipy.imaging.visualisation.vis_tools.reorientateVectorField(axis, u, v, w)
    -
    - -
    -
    -platipy.imaging.visualisation.vis_tools.resampleVF(axis, index, skipStep)
    -
    - -
    -
    -platipy.imaging.visualisation.vis_tools.returnSlice(axis, index)
    -
    - -
    -
    -platipy.imaging.visualisation.vis_tools.returnVectorFieldSlice(axis, index)
    +
    +
    +clear()
    +

    Clear all overlays

    +
    +
    +
    +draw_bounding_box_on_axes(ax, view, box, add_label=True)
    - -
    -
    -platipy.imaging.visualisation.vis_tools.returnVectorSlice(skipStep)
    +
    +
    +property image
    - -
    -
    -platipy.imaging.visualisation.vis_tools.vectorImageGrid(axis, vf)
    +
    +
    +property labelmap
    - -
    -
    +
    +
    +set_limits_from_label(label, expansion=[0, 0, 0])
    +

    Sets the limits of the axes to the bounds of the given label.

    +
    +
    Parameters
    +
      +
    • label (sitk.Image) – The label around which to set the limits

    • +
    • expansion (list, optional) – Expansion (in mm) around the label. Defaults to [0, 0, 0].

    • +
    +
    +
    +
    +
    +
    +show(interact=False)
    +

    Render the image with all overlays

    +
    + + +

    Module contents

    -
    -
    - - - - -
    - + + -
    - -
    -

    - © Copyright 2020, Phillip Chlap & Rob Finnegan. - -

    -
    + +
    + + -
    + + +
    + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/genindex.html b/docs/site/genindex.html index a0acb5bf..14e6d5e6 100644 --- a/docs/site/genindex.html +++ b/docs/site/genindex.html @@ -1,2108 +1,2099 @@ + + + + + + + Index - PlatiPy documentation + + + + - - - - - - + + - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
    +
    + +
    + +
    + +
    +
    + +
    +
    +
    + - - - - - -
    -
    -
    -
    - - -

    Index

    - -
    - Symbols - | A - | B - | C - | D - | E - | F - | G - | H - | I - | K - | L - | M - | N - | O - | P - | R - | S - | T - | V - | W - -
    -

    Symbols

    - - +
    +
  • + -v -

    A

    - - +
    + + +
    +

    A

    + + - + -
    +
  • Algorithm (class in platipy.backend.application)
  • -
  • AlgorithmEndpoint (class in platipy.backend.api) +
  • +
  • augment() (platipy.imaging.generation.augment.ContractAugment method) -

    B

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  • authenticate() (in module platipy.backend.api)
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    + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/getting_started.html b/docs/site/getting_started.html new file mode 100644 index 00000000..2b1962dc --- /dev/null +++ b/docs/site/getting_started.html @@ -0,0 +1,254 @@ + + + + + + + + + Getting Started - PlatiPy documentation + + + + + + + + + + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
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    Getting Started

    +
    +

    Installation

    +

    Currently this repository is not available in the official Python repositories. However, you can +install it with pip using the following command:

    +

    pip install git+git://github.com/pyplati/platipy

    +

    If you have already installed the library, you can install the latest updates using:

    +

    pip install --upgrade git+git://github.com/pyplati/platipy

    +
    +
    +

    Getting Started

    +
    +

    Command Line Interface (CLI)

    +

    Once you have installed PlatiPy, the platipy command should be available in your environment. +Try the following command to check that is is working:

    +

    platipy --help

    +

    This lists the different tools which are available via the command line interface. Learn more about +these tools and how to use them here: here.

    +
    +
    +

    Python

    +
    +

    Jupyter Notebooks

    +

    The following Jupyter Notebooks are provided to demonstrate how to use some of the PlatiPy +functionality from your Python code:

    + +
    +
    +
    +
    + +
    + +
    + +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/docs/site/index.html b/docs/site/index.html index 8cf3b66c..570047aa 100644 --- a/docs/site/index.html +++ b/docs/site/index.html @@ -1,290 +1,197 @@ + + + + + + + + + Home - PlatiPy documentation + + + + - - - - - - - - - PlatiPy documentation — PlatiPy documentation - - - + + - - - - - - - - + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
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    + +

    PlatiPy documentation

    -

    PlatiPy is a Processing Library and Analysis Toolkit for Medical Imaging in Python. It contains a collections of tools and scripts useful for processing and analysis of medical images.

    Generally these tools are accesible via a Command Line Interface or by calling them in a Python script directly. Examples are provided in Jupyter Notebooks to demonstrate how to use them from within Python code.

    -
    -

    Installation

    -

    Currently this repository is not available in the official Python repositories. However, you can -install it with pip using the following command:

    -

    pip install git+git://github.com/pyplati/platipy

    -

    If you have already installed the library, you can install the latest updates using:

    -

    pip install --upgrade git+git://github.com/pyplati/platipy

    -
    -
    -

    Getting Started

    -
    -

    Command Line Interface (CLI)

    -

    Once you have installed PlatiPy, the platipy command should be available in your environment. -Try the following command to check that is is working:

    -

    platipy --help

    -

    This lists the different tools which are available via the command line interface. Learn more about -these tools and how to use them here: here.

    -
    -
    -

    Python

    -
    -

    Jupyter Notebooks

    -

    The following Jupyter Notebooks are provided to demonstrate how to use some of the PlatiPy -functionality from your Python code:

    - -
    -
    -

    Module Reference

    -

    You can find more detailed documentation on how to use the different classes and functions found in -PlatiPy here.

    -
    -
    +
    -
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    - © Copyright 2020, Phillip Chlap & Rob Finnegan. - -

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\ No newline at end of file diff --git a/docs/site/visualiser.html b/docs/site/visualiser.html new file mode 100644 index 00000000..58b21d38 --- /dev/null +++ b/docs/site/visualiser.html @@ -0,0 +1,341 @@ + + + + + + + + + Image Visualiser - PlatiPy documentation + + + + + + + + + + + + Contents + + + + + + + + + Menu + + + + + + + + Expand + + + + + + + + + + + + + +
    +
    +
    + +
    + +
    + +
    +
    + +
    +
    +
    + +
    +

    Image Visualiser

    +
    +
    +class platipy.imaging.visualisation.visualiser.ImageVisualiser(image, cut=None, axis='ortho', window=[-250, 500], figure_size_in=10, limits=None, colormap=<matplotlib.colors.LinearSegmentedColormap object>, origin='normal', projection=False)
    +

    Bases: object

    +

    Class to assist with visualising images and overlaying contours, scalars and bounding +boxes.

    +
    +
    +add_bounding_box(bounding_box, name=None, color='r', linewidth=2)
    +

    Add a bounding box to draw.

    +
    +
    Parameters
    +
      +
    • bounding_box (dict|list|tuple|sitk.Image) – Dictionary of bounding boxes where list key +is the name of the bounding box. If list or tuple then pass +bounding box in format (x, y, z, w, h, d). If sitk.Image then +mask is assumed and bounding box around this mask is used.

    • +
    • name (list, optional) – Name of this bounding box if bounding_box is list, tuple or +sitk.Image. Defaults to None.

    • +
    • color (str|tuple|list, optional) – The color to use when drawing the bounding box(es). +Defaults to None.

    • +
    • linewidth (int, optional) – The line width of the bounding box(es). Defaults to 2.

    • +
    +
    +
    Raises
    +

    ValueError – Raised if input is not a list or tuple of length 6 or and sitk.Image.

    +
    +
    +
    +
    +
    +add_comparison_overlay(image, name=None, color_rotation=0.35)
    +

    Overlay a comparison image on the existing image

    +
    +
    Parameters
    +
      +
    • sitk.Image) (image) – Image (will be displayed as a comparison).

    • +
    • name (str, optional) – Name to give the image. Defaults to None.

    • +
    • color_rotation (float, optional) – Defines the hue of the original image (0 - 0.5).

    • +
    +
    +
    Raises
    +

    ValueError – Comparison overlay must be of type sitk.Image.

    +
    +
    +
    +
    +
    +add_contour(contour, name=None, color=None, colormap=<matplotlib.colors.LinearSegmentedColormap object>, linewidth=2, show_legend=True)
    +

    Add a contour as overlay

    +
    +
    Parameters
    +
      +
    • contour (sitk.Image|dict) – Contour mask or dict containing contour masks.

    • +
    • name (str, optional) – Name to give the contour (only used if passing sitk.Image as +contour). Defaults to None.

    • +
    • color (str|tuple|list, optional) – The color to use when drawing the contour(s). +Defaults to None.

    • +
    +
    +
    Raises
    +
      +
    • ValueError – Contour must be dict of sitk.Image.

    • +
    • ValueError – If passing a dict for contour, all values must be sitk.Image.

    • +
    +
    +
    +
    +
    +
    +add_scalar_overlay(scalar_image, name=None, colormap=<matplotlib.colors.LinearSegmentedColormap object>, alpha=0.75, min_value=False, max_value=False, discrete_levels=False, mid_ticks=False, show_colorbar=True, norm=None)
    +

    Overlay a scalar image on to the existing image

    +
    +
    Parameters
    +
      +
    • sitk.Image|dict) (scalar_image) – Scalar image or dict containing scalar images.

    • +
    • name (str, optional) – Name to give the scalar image (only used if passing sitk.Image as +scalar image). Defaults to None.

    • +
    • colormap (matplotlib.colors.Colormap, optional) – The colormap to be used when +overlaying this scalar image. Defaults +to plt.cm.get_cmap(“Spectral”).

    • +
    • alpha (float, optional) – Alpha to apply to overlay. Defaults to 0.75.

    • +
    • min_value (float, optional) – Values below this value aren’t rendered. Defaults to 0.1.

    • +
    +
    +
    Raises
    +
      +
    • ValueError – Scalar overlay must be dict of sitk.Image.

    • +
    • ValueError – If passing a dict for contour, all values must be sitk.Image.

    • +
    +
    +
    +
    +
    +
    +add_vector_overlay(vector_image, name=None, colormap=<matplotlib.colors.LinearSegmentedColormap object>, alpha=0.75, arrow_scale=0.25, arrow_width=1, subsample=4, color_function='perpendicular', show_colorbar=True)
    +

    Overlay a vector field on to the existing image

    +
    +
    Parameters
    +
      +
    • sitk.Image|dict) (vector_image) – Vector image (will be displayed as ).

    • +
    • name (str, optional) – Name to give the vector field (only used if passing +sitk.Image as vector field). Defaults to None.

    • +
    • colormap (matplotlib.colors.Colormap, optional) – The colormap to be used when +overlaying this vector field. Defaults +to plt.cm.get_cmap(“Spectral”).

    • +
    • alpha (float, optional) – Alpha to apply to overlay vectors. Defaults to 0.75.

    • +
    • arrow_scale (float, optional) – Relative scaling of vectors. Defaults to 0.25.

    • +
    • arrow_width (float, optional) – Width of vector field arrow. Defaults to 0.25.

    • +
    • subsample (int, optional) – Defines to subsampling ratio of displayed vectors. +Defaults to 4.

    • +
    • color_function (str, optional) – Determines how vectors are colored. Options: +‘perpendicular’ - vectors colored by perpendicular +value +‘magnitude’ - vectors colored by magnitude.

    • +
    +
    +
    Raises
    +

    ValueError – Vector overlay must be of type sitk.Image.

    +
    +
    +
    +
    +
    +clear()
    +

    Clear all overlays

    +
    +
    +
    +set_limits_from_label(label, expansion=[0, 0, 0])
    +

    Sets the limits of the axes to the bounds of the given label.

    +
    +
    Parameters
    +
      +
    • label (sitk.Image) – The label around which to set the limits

    • +
    • expansion (list, optional) – Expansion (in mm) around the label. Defaults to [0, 0, 0].

    • +
    +
    +
    +
    +
    +
    +show(interact=False)
    +

    Render the image with all overlays

    +
    +
    +
    + +
    + +
    + +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/docs/visualiser.rst b/docs/visualiser.rst new file mode 100644 index 00000000..f1bd7568 --- /dev/null +++ b/docs/visualiser.rst @@ -0,0 +1,7 @@ +##################### +Image Visualiser +##################### + +.. autoclass:: platipy.imaging.visualisation.visualiser.ImageVisualiser + :members: + :show-inheritance: diff --git a/examples/generate_synthetic_deformations/README.md b/examples/README.md similarity index 58% rename from examples/generate_synthetic_deformations/README.md rename to examples/README.md index 74016b4e..0f1d316e 100644 --- a/examples/generate_synthetic_deformations/README.md +++ b/examples/README.md @@ -1,3 +1,7 @@ +# Download from TCIA Example + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pyplati/platipy/blob/master/platipy/examples/download/tcia.ipynb) + # Examples to Generate Synthetic Deformations for Head & Neck [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pyplati/platipy/blob/master/examples/generate_synthetic_deformations/generate_synthetic_head_neck_deformation.ipynb) diff --git a/examples/animation.gif b/examples/animation.gif new file mode 100644 index 0000000000000000000000000000000000000000..8240bddacb29067eed073bedd3fd696ca98bb391 GIT binary patch literal 3575173 zcmX8Zbx;(K|3B~xIF6%^?(URskUAPXIs~LSx*PTAj-$J~8$?9%Xr)V3x&#G81>?<^ z&+qq`ea`O8&g?%Mv-{eohL*atj9ohr35*8-{{8#+?c2B0)6@O^{SO~LtgWprFE1}H zE-oxAEWZDrckkZK&(F`z&CSlvF3vodeaQ6m-1PMH{N&`+)Xdb>)WdpmauSQh&W(*t zOiYZAPmYg|kBv=^jg5_tj*pIxj*Lu>jEoEq55Il;c4%nm&6_uagM+VMzaAJE=+9?7?d|F5>FVn0e9+O+{-CX`rKP30xw)ySsj;!KzP`S$uCBJWw&p=qRb^#mMMXt< zd3jk`SxHGradBU9adBzEgX*H9qWt`#qQd_vDEMC<>6T($dpQF&Io*8YUw>BP0EP($dmWQ~#F-P07hA zDS0Umk{?t)q%i40?n8>7KTk|7OneX!5FcNVkdP1`|G)fC-2cSHWX8nA#J_m)@?~Lk zbaYfy6dD~B`2hVtFJ7S0FaPIZ>%sHqFP}eu9uW}{99$3{9v&PV9u^iB6cmOI4Gjqi zi46Xqpr9ah;Qs^!1o->==efH2`DOU|`T6>0`TF|$_@w#x_;`7xdV70&d3kwwU_3oN zJv=b z0X_{BN(_NOadGi*aQty_aKK#>92z&_y0s(-B!vk3F3G@-hMykq4gs6}( z7q9*KD_a8x8%%J4pS_S}y2xp@d&b|3i<{bP}-X zhc+dzelE>whv>m13CrWK%LNQLo`_DwrX3ksIi5hup?lK(iggr(i$@5RK$Zi5AOw*B z%9eF3j)Yym+V)B%j)+k)il$&MHnm){KsHgfhmT6oW+aL8X%JCSukXRT2KQgYr5|61 z>`r``?R)g>_0?C|i2POtNoU&78Zz8Q@;Y7CU-|e(Slz-|=RwC!kDBikJi7TD_$A{B zu3X4k$KBB_g1q^rXTiOqv56Sg+EihKkAy8rqSifxQbtcL|osJ>UYsd!NI6e zSj1q|8EUQ#G}&_k47K@tZVYwBRs)uyoaf@B5aPcSD@X)m&WfR)0_Cc)iFMAZsr7Tp zHFKwuoHa|&H;3%jp`2&ymEk~BX1h36(-iv@83-+V=AE-eOrh@)EvgceOYb(=^RqCi zGt`;ht1jm()_WNL-mKVnig&Wue_>U#ByhuZvLtvXQKK~UWIp(F$JJ*1x~`TbKMBY|xWwP*$CcA~yQOKOA!ikMTMSWt%0 zm@m|Vpo6@|1_I3O*O1Mn>FP2nl^IzHg(h(jF2qqLANsHH$Ij&h3=$x8~KdCC( z$0O~gVhu_W#-ZbZX{PgtPBDk9Tw#0dV(eI5v~J7Ch$J>HD~JUWL!ybqvh%@k?~_}yx!h7`2)bBQ(%gBfXu223)>*R3AGfm-wly-s z@1Mj<9x7LeSJOt31y;!)TG)B{6@A!l2H-B#3nG-ajhhcx@Z@hIed1=vxuh}DCe8L{ zF4OOV2BHaP7*pl9r18SUCtt~me(?{oTYC=88cMR=r_0<*IAh2ySA|xR5Jw?t(Wu@{ z^AV`R)X1ezIXL;^N#=MKkSOg~(y#?f;F>W;T@{b74>+S-W3?2?_9gNyW@r4hrK50d zP|eO*{p^6BIrE*=fM5N$bQ;&sfA*x_{=OI?P@h8TOdV#@-IAc)JC(px<((BAaD75? 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"execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "FOR TESTING PURPOSES\n", - "\"\"\"\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "Import relevant modules\n", - "\"\"\"\n", - "\n", - "import pathlib\n", - "\n", - "import SimpleITK as sitk\n", - "import numpy as np\n", - "\n", - "from platipy.imaging.registration.registration import apply_field\n", - "\n", - "from platipy.imaging.visualisation.tools import ImageVisualiser, project_onto_arbitrary_plane, generate_animation_from_image_sequence\n", - "\n", - "from platipy.imaging.utils.tools import label_to_roi, get_com\n", - "\n", - "from platipy.imaging.tests.pull_data import get_lung_data\n", - "\n", - "\"\"\"\n", - "Deformation field operations\n", - "\"\"\"\n", - "from platipy.imaging.deformation_fields.deformation_field_operations import (\n", - "get_bone_mask,\n", - "get_external_mask,\n", - "generate_field_shift,\n", - "generate_field_asymmetric_contract,\n", - "generate_field_asymmetric_extend,\n", - "generate_field_expand,\n", - "generate_field_radial_bend\n", - ")\n", - "\n", - "%matplotlib notebook" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "Read in some images and structures for this demonstration\n", - "We are using a lung radiotherapy dataset comprising CT and RTSTRUCT\n", - "\"\"\"\n", - "\n", - "image_ct = sitk.ReadImage(\"../../imaging/tests/data/LCTSC/LCTSC-Test-S1-101/CT.nii.gz\")\n", - "\n", - "structure_list = [\"Heart\", \"Esophagus\", \"Lung_L\", \"Lung_R\", \"SpinalCord\"]\n", - "label_images = {i:sitk.ReadImage(f\"../../imaging/tests/data/LCTSC/LCTSC-Test-S1-101/Struct_{i}.nii.gz\") for i in structure_list}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "external_mask = get_external_mask(image_ct, dilate=5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "label_expand, expand_transform, expand_dvf = generate_field_radial_bend(\n", - " image_ct,\n", - " external_mask,\n", - " reference_point=get_com(label_images[\"Heart\"]),\n", - " axis_of_rotation=[0,0,1],\n", - " scale=0.5\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "interp_list = np.linspace(0, 1, 10)\n", - "image_list = []\n", - "scalar_list = []\n", - "\n", - "for index, interp_value in enumerate(interp_list):\n", - " \n", - " print(index, interp_value)\n", - " \n", - "\n", - " \"\"\"\n", - " Generate interpolated DVF\n", - " \"\"\"\n", - "\n", - " expand_dvf_interpolate = sitk.Compose(*[float(interp_value)*sitk.VectorIndexSelectionCast(expand_dvf, i, sitk.sitkFloat64) for i in [0,1,2]])\n", - " expand_transform_interpolate = sitk.DisplacementFieldTransform( expand_dvf_interpolate )\n", - "\n", - " #print(\" Interpolation complete.\")\n", - "\n", - " \"\"\"\n", - " Generate deformed and projected image\n", - " \"\"\"\n", - "\n", - " im_deform = apply_field(image_ct, expand_transform_interpolate, interp=2)\n", - "\n", - "\n", - " im_project = project_onto_arbitrary_plane(\n", - " im_deform,\n", - " projection_name='mean',\n", - " rotation_axis=[0,0,1],\n", - " rotation_angle=0,\n", - " projection_axis=0\n", - " )\n", - "\n", - " image_list.append(im_project)\n", - " \n", - " #print(\" Deformation and projection complete.\")\n", - "\n", - "# \"\"\"\n", - "# Generate deformed and projected scalar field (Heart contour)\n", - "# \"\"\"\n", - "\n", - "# scalar_deform = apply_field(label_images[\"Lung_L\"]+label_images[\"Lung_R\"], expand_transform_interpolate, structure=True, default_value=0, interp=1)\n", - "\n", - "# scalar_rotate = project_onto_arbitrary_plane(\n", - "# scalar_deform,\n", - "# projection_name='mean',\n", - "# rotation_axis=[0,0,1],\n", - "# rotation_angle=0,\n", - "# projection_axis=1\n", - "# )\n", - "\n", - "# scalar_list.append(scalar_rotate)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "animation = generate_animation_from_image_sequence(image_list + image_list[::-1], \"animation_twist.gif\", scalar_list=False, fps=25)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "angle_list = np.linspace(0, np.pi*2, 50)\n", - "image_list = []\n", - "contour_list = []\n", - "scalar_list = []\n", - "\n", - "for angle in angle_list:\n", - " im_rotate = project_onto_arbitrary_plane(\n", - " image_ct,\n", - " projection_name='mean',\n", - " rotation_axis=[0,0,1],\n", - " rotation_angle=angle,\n", - " projection_axis=1\n", - " )\n", - " image_list.append(im_rotate)\n", - " \n", - " contour_rotate_lung_l = project_onto_arbitrary_plane(\n", - " label_images[\"Lung_L\"],\n", - " projection_name='max',\n", - " rotation_axis=[0,0,1],\n", - " rotation_angle=angle,\n", - " projection_axis=1,\n", - " resample_interpolation=1,\n", - " default_value=0 \n", - " )\n", - " \n", - " contour_rotate_lung_r= project_onto_arbitrary_plane(\n", - " label_images[\"Lung_R\"],\n", - " projection_name='max',\n", - " rotation_axis=[0,0,1],\n", - " rotation_angle=angle,\n", - " projection_axis=1,\n", - " resample_interpolation=1,\n", - " default_value=0 \n", - " )\n", - " \n", - " contour_list.append({\"LUNG L\": contour_rotate_lung_l, \"LUNG R\": contour_rotate_lung_r})\n", - " \n", - " scalar_rotate = project_onto_arbitrary_plane(\n", - " label_images[\"Heart\"],\n", - " projection_name='mean',\n", - " rotation_axis=[0,0,1],\n", - " rotation_angle=angle,\n", - " projection_axis=1,\n", - " resample_interpolation=1,\n", - " default_value=0 \n", - " )\n", - " \n", - " combined_scalar = 1*contour_rotate_lung_l + 2*contour_rotate_lung_r + 3+scalar_rotate\n", - " \n", - " scalar_list.append(scalar_rotate)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "animation = generate_animation_from_image_sequence(image_list, contour_list=contour_list, scalar_list=scalar_list, scalar_alpha=1, scalar_min=0.01, fps=12.5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "interp_list = np.linspace(0, 1, 25)\n", - "image_list = []\n", - "scalar_list = []\n", - "\n", - "for index, interp_value in enumerate(interp_list):\n", - " \n", - " print(index, interp_value)\n", - " \n", - "\n", - " \"\"\"\n", - " Generate interpolated DVF\n", - " \"\"\"\n", - "\n", - " expand_dvf_interpolate = sitk.Compose(*[float(interp_value)*sitk.VectorIndexSelectionCast(expand_dvf, i, sitk.sitkFloat64) for i in [0,1,2]])\n", - " expand_transform_interpolate = sitk.DisplacementFieldTransform( expand_dvf_interpolate )\n", - "\n", - " #print(\" Interpolation complete.\")\n", - "\n", - " \"\"\"\n", - " Generate deformed and projected image\n", - " \"\"\"\n", - "\n", - " im_deform = apply_field(image_ct, expand_transform_interpolate, interp=2)\n", - "\n", - "\n", - " im_project = project_onto_arbitrary_plane(\n", - " im_deform,\n", - " projection_name='mean',\n", - " rotation_axis=[0,0,1],\n", - " rotation_angle=0,\n", - " projection_axis=1\n", - " )\n", - "\n", - " image_list.append(im_project)\n", - " \n", - " #print(\" Deformation and projection complete.\")\n", - "\n", - " \"\"\"\n", - " Generate deformed and projected scalar field (Heart contour)\n", - " \"\"\"\n", - "\n", - " scalar_deform = apply_field(label_images[\"Lung_L\"]+label_images[\"Lung_R\"], expand_transform_interpolate, structure=True, default_value=0, interp=1)\n", - "\n", - " scalar_rotate = project_onto_arbitrary_plane(\n", - " scalar_deform,\n", - " projection_name='mean',\n", - " rotation_axis=[0,0,1],\n", - " rotation_angle=0,\n", - " projection_axis=1\n", - " )\n", - "\n", - " scalar_list.append(scalar_rotate)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/bronchus_segmentation/bronchus_segmentation.ipynb b/examples/bronchus_segmentation.ipynb similarity index 100% rename from examples/bronchus_segmentation/bronchus_segmentation.ipynb rename to examples/bronchus_segmentation.ipynb diff --git a/examples/service/bronchus.ipynb b/examples/bronchus_service.ipynb similarity index 100% rename from examples/service/bronchus.ipynb rename to examples/bronchus_service.ipynb diff --git a/examples/cardiac_atlas_segmentation/cardiac_segmentation.ipynb b/examples/cardiac_segmentation.ipynb similarity index 100% rename from examples/cardiac_atlas_segmentation/cardiac_segmentation.ipynb rename to examples/cardiac_segmentation.ipynb diff --git a/examples/service/cardiac.ipynb b/examples/cardiac_service.ipynb similarity index 100% rename from examples/service/cardiac.ipynb rename to examples/cardiac_service.ipynb diff --git a/examples/download/README.md b/examples/download/README.md deleted file mode 100644 index ef999537..00000000 --- a/examples/download/README.md +++ /dev/null @@ -1,3 +0,0 @@ -# Download from TCIA Example - -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pyplati/platipy/blob/master/platipy/examples/download/tcia.ipynb) diff --git a/examples/generate_animations.ipynb b/examples/generate_animations.ipynb new file mode 100644 index 00000000..94882eb4 --- /dev/null +++ b/examples/generate_animations.ipynb @@ -0,0 +1,316 @@ +{ + "cells": [ + { + "source": [ + "## Animation Generation Example" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " import platipy\n", + "except:\n", + " !pip install git+https://github.com/pyplati/platipy.git\n", + " import platipy\n", + "\n", + "import pathlib\n", + "\n", + "import SimpleITK as sitk\n", + "import numpy as np\n", + "\n", + "from platipy.imaging.registration.utils import apply_transform\n", + "\n", + "from platipy.imaging.visualisation.visualiser import ImageVisualiser\n", + "from platipy.imaging.visualisation.utils import project_onto_arbitrary_plane\n", + "from platipy.imaging.visualisation.animation import generate_animation_from_image_sequence\n", + "\n", + "from platipy.imaging.utils.crop import label_to_roi\n", + "from platipy.imaging.label.utils import get_com\n", + "\n", + "from platipy.imaging.tests.data import get_lung_nifti\n", + "\n", + "\"\"\"\n", + "Deformation field operations\n", + "\"\"\"\n", + "from platipy.imaging.generation.mask import (\n", + "get_bone_mask,\n", + "get_external_mask)\n", + "\n", + "from platipy.imaging.generation.dvf import (\n", + "generate_field_shift,\n", + "generate_field_asymmetric_contract,\n", + "generate_field_asymmetric_extend,\n", + "generate_field_expand,\n", + "generate_field_radial_bend\n", + ")\n", + "\n", + "%matplotlib inline" + ] + }, + { + "source": [ + "## Load data\n", + "\n", + "Read in some images and structures for this demonstration\n", + "We are using a lung radiotherapy dataset comprising CT and RTSTRUCT" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "nifti_data = get_lung_nifti()\n", + "\n", + "image_ct = sitk.ReadImage(str(nifti_data.joinpath(\"LCTSC-Test-S1-101/IMAGES/LCTSC_TEST_S1_101_0_CT_0.nii.gz\")))\n", + "\n", + "structure_list = [\"HEART\", \"ESOPHAGUS\", \"LUNG_L\", \"LUNG_R\", \"SPINALCORD\"]\n", + "label_images = {i:sitk.ReadImage(str(nifti_data.joinpath(f\"LCTSC-Test-S1-101/STRUCTURES/LCTSC_TEST_S1_101_0_RTSTRUCT_{i}.nii.gz\"))) for i in structure_list}" + ] + }, + { + "source": [ + "## Get the an external mask and generate a radial bend" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "external_mask = get_external_mask(image_ct, dilate=5)\n", + "\n", + "label_expand, expand_transform, expand_dvf = generate_field_radial_bend(\n", + " image_ct,\n", + " external_mask,\n", + " reference_point=get_com(label_images[\"HEART\"]),\n", + " axis_of_rotation=[0,0,1],\n", + " scale=0.5\n", + ")" + ] + }, + { + "source": [ + "## Interpolate at different positions for the animation" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "0 0.0\n", + "1 0.1111111111111111\n", + "2 0.2222222222222222\n", + "3 0.3333333333333333\n", + "4 0.4444444444444444\n", + "5 0.5555555555555556\n", + "6 0.6666666666666666\n", + "7 0.7777777777777777\n", + "8 0.8888888888888888\n", + "9 1.0\n" + ] + } + ], + "source": [ + "interp_list = np.linspace(0, 1, 10)\n", + "image_list = []\n", + "scalar_list = []\n", + "\n", + "for index, interp_value in enumerate(interp_list):\n", + " \n", + " print(index, interp_value)\n", + " \n", + "\n", + " \"\"\"\n", + " Generate interpolated DVF\n", + " \"\"\"\n", + "\n", + " expand_dvf_interpolate = sitk.Compose(*[float(interp_value)*sitk.VectorIndexSelectionCast(expand_dvf, i, sitk.sitkFloat64) for i in [0,1,2]])\n", + " expand_transform_interpolate = sitk.DisplacementFieldTransform( expand_dvf_interpolate )\n", + "\n", + " #print(\" Interpolation complete.\")\n", + "\n", + " \"\"\"\n", + " Generate deformed and projected image\n", + " \"\"\"\n", + "\n", + " im_deform = apply_transform(image_ct, transform=expand_transform_interpolate, interpolator=sitk.sitkLinear)\n", + "\n", + "\n", + " im_project = project_onto_arbitrary_plane(\n", + " im_deform,\n", + " projection_name='mean',\n", + " rotation_axis=[0,0,1],\n", + " rotation_angle=0,\n", + " projection_axis=0\n", + " )\n", + "\n", + " image_list.append(im_project)\n", + " " + ] + }, + { + "source": [ + "## Generate the Animation" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "

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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "animation = generate_animation_from_image_sequence(image_list + image_list[::-1], \"animation_twist.gif\", scalar_list=None, fps=25)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "angle_list = np.linspace(0, np.pi*2, 50)\n", + "image_list = []\n", + "contour_list = []\n", + "scalar_list = []\n", + "\n", + "for angle in angle_list:\n", + " im_rotate = project_onto_arbitrary_plane(\n", + " image_ct,\n", + " projection_name='mean',\n", + " rotation_axis=[0,0,1],\n", + " rotation_angle=angle,\n", + " projection_axis=1\n", + " )\n", + " image_list.append(im_rotate)\n", + " \n", + " contour_rotate_lung_l = project_onto_arbitrary_plane(\n", + " label_images[\"LUNG_L\"],\n", + " projection_name='max',\n", + " rotation_axis=[0,0,1],\n", + " rotation_angle=angle,\n", + " projection_axis=1,\n", + " resample_interpolation=1,\n", + " default_value=0 \n", + " )\n", + " \n", + " contour_rotate_lung_r= project_onto_arbitrary_plane(\n", + " label_images[\"LUNG_R\"],\n", + " projection_name='max',\n", + " rotation_axis=[0,0,1],\n", + " rotation_angle=angle,\n", + " projection_axis=1,\n", + " resample_interpolation=1,\n", + " default_value=0 \n", + " )\n", + " \n", + " contour_list.append({\"LUNG L\": contour_rotate_lung_l, \"LUNG R\": contour_rotate_lung_r})\n", + " \n", + " scalar_rotate = project_onto_arbitrary_plane(\n", + " label_images[\"HEART\"],\n", + " projection_name='mean',\n", + " rotation_axis=[0,0,1],\n", + " rotation_angle=angle,\n", + " projection_axis=1,\n", + " resample_interpolation=1,\n", + " default_value=0 \n", + " )\n", + " \n", + " combined_scalar = 1*contour_rotate_lung_l + 2*contour_rotate_lung_r + 3+sitk.Cast(scalar_rotate, sitk.sitkUInt8)\n", + " \n", + " scalar_list.append(scalar_rotate)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "No contour levels were found within the data range.\n", + "No contour levels were found within the data range.\n", + "No contour levels were found within the data range.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
    ", + "image/svg+xml": "\n\n\n\n \n \n \n \n 2021-05-25T06:09:38.701794\n image/svg+xml\n \n \n Matplotlib v3.3.4, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "animation = generate_animation_from_image_sequence(image_list, contour_list=contour_list, scalar_list=scalar_list, scalar_alpha=1, scalar_min=0.01, fps=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/examples/generate_synthetic_deformations/generate_animations.ipynb b/examples/generate_synthetic_deformations/generate_animations.ipynb deleted file mode 100644 index f031a509..00000000 --- a/examples/generate_synthetic_deformations/generate_animations.ipynb +++ /dev/null @@ -1,405 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "FOR TESTING PURPOSES\n", - "\"\"\"\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "Import relevant modules\n", - "\"\"\"\n", - "\n", - "import pathlib\n", - "\n", - "import SimpleITK as sitk\n", - "import numpy as np\n", - "\n", - "from platipy.imaging.registration.registration import apply_field\n", - "\n", - "from platipy.imaging.visualisation.tools import ImageVisualiser, project_onto_arbitrary_plane, generate_animation_from_image_sequence\n", - "\n", - "from platipy.imaging.utils.tools import label_to_roi, get_com\n", - "\n", - "from platipy.imaging.tests.pull_data import get_lung_data\n", - "\n", - "\"\"\"\n", - "Deformation field operations\n", - "\"\"\"\n", - "from platipy.imaging.deformation_fields.deformation_field_operations import (\n", - "get_bone_mask,\n", - "get_external_mask,\n", - "generate_field_shift,\n", - "generate_field_asymmetric_contract,\n", - "generate_field_asymmetric_extend,\n", - "generate_field_expand,\n", - "generate_field_radial_bend\n", - ")\n", - "\n", - "%matplotlib notebook" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "Read in some images and structures for this demonstration\n", - "We are using a lung radiotherapy dataset comprising CT and RTSTRUCT\n", - "\"\"\"\n", - "\n", - "image_ct = sitk.ReadImage(\"../../imaging/tests/data/LCTSC/LCTSC-Test-S1-101/CT.nii.gz\")\n", - "\n", - "structure_list = [\"Heart\", \"Esophagus\", \"Lung_L\", \"Lung_R\", \"SpinalCord\"]\n", - "label_images = {i:sitk.ReadImage(f\"../../imaging/tests/data/LCTSC/LCTSC-Test-S1-101/Struct_{i}.nii.gz\") for i in structure_list}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "external_mask = get_external_mask(image_ct, dilate=5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "label_expand, expand_transform, expand_dvf = generate_field_radial_bend(\n", - " image_ct,\n", - " external_mask,\n", - " reference_point=get_com(label_images[\"Heart\"]),\n", - " axis_of_rotation=[0,1,0],\n", - " scale=0.5\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "interp_list = np.linspace(0, 1, 10)\n", - "image_list = []\n", - "scalar_list = []\n", - "\n", - "for index, interp_value in enumerate(interp_list):\n", - " \n", - " print(index, interp_value)\n", - " \n", - "\n", - " \"\"\"\n", - " Generate interpolated DVF\n", - " \"\"\"\n", - "\n", - " expand_dvf_interpolate = sitk.Compose(*[float(interp_value)*sitk.VectorIndexSelectionCast(expand_dvf, i, sitk.sitkFloat64) for i in [0,1,2]])\n", - " expand_transform_interpolate = sitk.DisplacementFieldTransform( expand_dvf_interpolate )\n", - "\n", - " #print(\" Interpolation complete.\")\n", - "\n", - " \"\"\"\n", - " Generate deformed and projected image\n", - " \"\"\"\n", - "\n", - " im_deform = apply_field(image_ct, expand_transform_interpolate, interp=2)\n", - "\n", - "\n", - " im_project = project_onto_arbitrary_plane(\n", - " im_deform,\n", - " projection_name='mean',\n", - " rotation_axis=[0,0,1],\n", - " rotation_angle=0,\n", - " projection_axis=0\n", - " )\n", - "\n", - " image_list.append(im_project)\n", - " \n", - " #print(\" Deformation and projection complete.\")\n", - "\n", - "# \"\"\"\n", - "# Generate deformed and projected scalar field (Heart contour)\n", - "# \"\"\"\n", - "\n", - "# scalar_deform = apply_field(label_images[\"Lung_L\"]+label_images[\"Lung_R\"], expand_transform_interpolate, structure=True, default_value=0, interp=1)\n", - "\n", - "# scalar_rotate = project_onto_arbitrary_plane(\n", - "# scalar_deform,\n", - "# projection_name='mean',\n", - "# rotation_axis=[0,0,1],\n", - "# rotation_angle=0,\n", - "# projection_axis=1\n", - "# )\n", - "\n", - "# scalar_list.append(scalar_rotate)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "animation = generate_animation_from_image_sequence(image_list + image_list[::-1], \"animation_twist.gif\", scalar_list=False, fps=25)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "angle_list = np.linspace(0, np.pi*2, 50)\n", - "image_list = []\n", - "contour_list = []\n", - "scalar_list = []\n", - "\n", - "for angle in angle_list:\n", - " im_rotate = project_onto_arbitrary_plane(\n", - " image_ct,\n", - " projection_name='mean',\n", - " rotation_axis=[0,0,1],\n", - " rotation_angle=angle,\n", - " projection_axis=1\n", - " )\n", - " image_list.append(im_rotate)\n", - " \n", - " contour_rotate_lung_l = project_onto_arbitrary_plane(\n", - " label_images[\"Lung_L\"],\n", - " projection_name='max',\n", - " rotation_axis=[0,0,1],\n", - " rotation_angle=angle,\n", - " projection_axis=1,\n", - " resample_interpolation=1,\n", - " default_value=0 \n", - " )\n", - " \n", - " contour_rotate_lung_r= project_onto_arbitrary_plane(\n", - " label_images[\"Lung_R\"],\n", - " projection_name='max',\n", - " rotation_axis=[0,0,1],\n", - " rotation_angle=angle,\n", - " projection_axis=1,\n", - " resample_interpolation=1,\n", - " default_value=0 \n", - " )\n", - " \n", - " contour_list.append({\"LUNG L\": contour_rotate_lung_l, \"LUNG R\": contour_rotate_lung_r})\n", - " \n", - " scalar_rotate = project_onto_arbitrary_plane(\n", - " label_images[\"Heart\"],\n", - " projection_name='mean',\n", - " rotation_axis=[0,0,1],\n", - " rotation_angle=angle,\n", - " projection_axis=1,\n", - " resample_interpolation=1,\n", - " default_value=0 \n", - " )\n", - " \n", - " combined_scalar = 1*contour_rotate_lung_l + 2*contour_rotate_lung_r + 3+scalar_rotate\n", - " \n", - " scalar_list.append(scalar_rotate)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "animation = generate_animation_from_image_sequence(image_list, contour_list=contour_list, scalar_list=scalar_list, scalar_alpha=1, scalar_min=0.01, fps=12.5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "interp_list = np.linspace(0, 1, 25)\n", - "image_list = []\n", - "scalar_list = []\n", - "\n", - "for index, interp_value in enumerate(interp_list):\n", - " \n", - " print(index, interp_value)\n", - " \n", - "\n", - " \"\"\"\n", - " Generate interpolated DVF\n", - " \"\"\"\n", - "\n", - " expand_dvf_interpolate = sitk.Compose(*[float(interp_value)*sitk.VectorIndexSelectionCast(expand_dvf, i, sitk.sitkFloat64) for i in [0,1,2]])\n", - " expand_transform_interpolate = sitk.DisplacementFieldTransform( expand_dvf_interpolate )\n", - "\n", - " #print(\" Interpolation complete.\")\n", - "\n", - " \"\"\"\n", - " Generate deformed and projected image\n", - " \"\"\"\n", - "\n", - " im_deform = apply_field(image_ct, expand_transform_interpolate, interp=2)\n", - "\n", - "\n", - " im_project = project_onto_arbitrary_plane(\n", - " im_deform,\n", - " projection_name='mean',\n", - " rotation_axis=[0,0,1],\n", - " rotation_angle=0,\n", - " projection_axis=1\n", - " )\n", - "\n", - " image_list.append(im_project)\n", - " \n", - " #print(\" Deformation and projection complete.\")\n", - "\n", - " \"\"\"\n", - " Generate deformed and projected scalar field (Heart contour)\n", - " \"\"\"\n", - "\n", - " scalar_deform = apply_field(label_images[\"Lung_L\"]+label_images[\"Lung_R\"], expand_transform_interpolate, structure=True, default_value=0, interp=1)\n", - "\n", - " scalar_rotate = project_onto_arbitrary_plane(\n", - " scalar_deform,\n", - " projection_name='mean',\n", - " rotation_axis=[0,0,1],\n", - " rotation_angle=0,\n", - " projection_axis=1\n", - " )\n", - "\n", - " scalar_list.append(scalar_rotate)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "deformed_ct, dvf_transform, dvf_field = generate_field_radial_bend(\n", - " ct,\n", - " external_mask,\n", - " (70,381,255),\n", - " axis_of_rotation=[0, 1, 0],\n", - " scale=0.1\n", - ")\n", - "\n", - "interp_list = np.linspace(0, 1, 10)\n", - "image_list = []\n", - "scalar_list = []\n", - "\n", - "for index, interp_value in enumerate(interp_list):\n", - " \n", - " print(index, interp_value)\n", - " \n", - "\n", - " \"\"\"\n", - " Generate interpolated DVF\n", - " \"\"\"\n", - "\n", - " expand_dvf_interpolate = sitk.Compose(*[float(interp_value)*sitk.VectorIndexSelectionCast(dvf_field, i, sitk.sitkFloat64) for i in [0,1,2]])\n", - " expand_transform_interpolate = sitk.DisplacementFieldTransform( expand_dvf_interpolate )\n", - "\n", - " #print(\" Interpolation complete.\")\n", - "\n", - " \"\"\"\n", - " Generate deformed and projected image\n", - " \"\"\"\n", - "\n", - " im_deform = apply_field(ct, expand_transform_interpolate, interp=2)\n", - "\n", - "\n", - " im_project = project_onto_arbitrary_plane(\n", - " im_deform,\n", - " projection_name='mean',\n", - " rotation_axis=[0,0,1],\n", - " rotation_angle=0,\n", - " projection_axis=0\n", - " )\n", - "\n", - " image_list.append(im_project)\n", - " \n", - " #print(\" Deformation and projection complete.\")\n", - "\n", - "# \"\"\"\n", - "# Generate deformed and projected scalar field (Heart contour)\n", - "# \"\"\"\n", - "\n", - "# scalar_deform = apply_field(label_images[\"Lung_L\"]+label_images[\"Lung_R\"], expand_transform_interpolate, structure=True, default_value=0, interp=1)\n", - "\n", - "# scalar_rotate = project_onto_arbitrary_plane(\n", - "# scalar_deform,\n", - "# projection_name='mean',\n", - "# rotation_axis=[0,0,1],\n", - "# rotation_angle=0,\n", - "# projection_axis=1\n", - "# )\n", - "\n", - "# scalar_list.append(scalar_rotate)\n", - "\n", - "animation = generate_animation_from_image_sequence(image_list + image_list[::-1], \"animation_twist.gif\", scalar_list=False, fps=25)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/generate_synthetic_deformations/generate_synthetic_head_neck_deformation.ipynb b/examples/generate_synthetic_head_neck_deformation.ipynb similarity index 90% rename from examples/generate_synthetic_deformations/generate_synthetic_head_neck_deformation.ipynb rename to examples/generate_synthetic_head_neck_deformation.ipynb index 80f1c8e8..cd35e588 100644 --- a/examples/generate_synthetic_deformations/generate_synthetic_head_neck_deformation.ipynb +++ b/examples/generate_synthetic_head_neck_deformation.ipynb @@ -9,31 +9,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ + "try:\n", + " import platipy\n", + "except:\n", + " !pip install git+https://github.com/pyplati/platipy.git\n", + " import platipy\n", + "\n", "import re\n", "from pathlib import Path\n", "\n", "import SimpleITK as sitk\n", "\n", "from platipy.dicom.download.tcia import get_hn_data\n", - "from platipy.dicom.dicom_directory_crawler.conversion_utils import process_dicom_directory\n", + "from platipy.dicom.io.crawl import process_dicom_directory\n", "\n", - "from platipy.imaging.visualisation.tools import ImageVisualiser\n", - "from platipy.imaging.utils.tools import get_com\n", - "from platipy.imaging.registration.registration import apply_field\n", + "from platipy.imaging.registration.utils import apply_transform\n", "\n", - "from platipy.imaging.deformation_fields.deformation_field_operations import (\n", - " get_external_mask,\n", - " get_bone_mask,\n", - " generate_field_radial_bend,\n", - " generate_field_shift,\n", - " generate_field_asymmetric_contract,\n", - " generate_field_asymmetric_extend,\n", - " generate_field_expand\n", - ")" + "from platipy.imaging.visualisation.visualiser import ImageVisualiser\n", + "from platipy.imaging.visualisation.utils import project_onto_arbitrary_plane\n", + "from platipy.imaging.visualisation.animation import generate_animation_from_image_sequence\n", + "\n", + "from platipy.imaging.utils.crop import label_to_roi\n", + "from platipy.imaging.label.utils import get_com\n", + "\n", + "from platipy.imaging.tests.data import get_lung_nifti\n", + "\n", + "from platipy.imaging.generation.mask import (\n", + "get_bone_mask,\n", + "get_external_mask)\n", + "\n", + "from platipy.imaging.generation.dvf import (\n", + "generate_field_shift,\n", + "generate_field_asymmetric_contract,\n", + "generate_field_asymmetric_extend,\n", + "generate_field_expand,\n", + "generate_field_radial_bend\n", + ")\n" ] }, { @@ -362,9 +377,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9-final" + "version": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file diff --git a/examples/generate_synthetic_deformations/generate_synthetic_deformations.ipynb b/examples/generate_synthetic_lung_deformations.ipynb similarity index 100% rename from examples/generate_synthetic_deformations/generate_synthetic_deformations.ipynb rename to examples/generate_synthetic_lung_deformations.ipynb diff --git a/examples/sample/1_convert_data.ipynb b/examples/sample/1_convert_data.ipynb deleted file mode 100644 index 42f421c5..00000000 --- a/examples/sample/1_convert_data.ipynb +++ /dev/null @@ -1,129 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dicom -> Nifti data conversion\n", - "\n", - "Before making use of the atlas based segmentation tools in this repository, all data should be converted from Dicom to Nifti as a first step. This Jupyter Notebook demonstrates how this can be done." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "import os\n", - "sys.path.append('../../..')\n", - "\n", - "import pydicom\n", - "\n", - "from platipy.dicom.rtstruct_to_nifti.convert import convert_rtstruct\n", - "\n", - "dicom_search_path = \"/path/to/dicom/data\" # Location where Dicom data is stored\n", - "data_path = './data' # Path where converted files should be placed" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**The following cell crawls the Dicom search path to find one series per directory**\n", - "\n", - "*Note: this was designed to be run on data that was exported from MIM*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data_objects = {}\n", - "\n", - "for root, dirs, files in os.walk(dicom_search_path, topdown=False):\n", - " for name in files:\n", - " f = os.path.join(root, name)\n", - " ds = pydicom.read_file(f, force=True)\n", - " \n", - " if not 'Modality' in ds:\n", - " continue\n", - " \n", - " case = ds.PatientID.split(' ')[1]\n", - "\n", - " if not case in data_objects:\n", - " data_objects[case] = {}\n", - " \n", - " p = f\n", - " if ds.Modality == 'CT':\n", - " p = os.path.dirname(f)\n", - " \n", - " do = {\n", - " 'seriesInstanceUID': ds.SeriesInstanceUID, \n", - " 'path': p}\n", - " data_objects[case][ds.Modality] = do\n", - " \n", - " break # Only read one file per dir\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Now that all Dicom data has been found, convert it to Nifti**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import SimpleITK as sitk\n", - "\n", - "for case in data_objects:\n", - " \n", - " case_dir = os.path.join(data_path, case)\n", - " if not os.path.exists(case_dir):\n", - " os.makedirs(case_dir)\n", - " do = data_objects[case]\n", - " \n", - " # Convert the CT and RT Struct\n", - " ct_path = do['CT']['path']\n", - " rtstruct_path = do['RTSTRUCT']['path']\n", - " convert_rtstruct(ct_path, rtstruct_path, output_img='CT.nii.gz', output_dir=case_dir)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/sample/2_inter_observer_variability.ipynb b/examples/sample/2_inter_observer_variability.ipynb deleted file mode 100644 index cb3bf24a..00000000 --- a/examples/sample/2_inter_observer_variability.ipynb +++ /dev/null @@ -1,192 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Interobserver Variability\n", - "\n", - "This Notebook demonstrates how to compute the interobserver variability of your Atlas data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "import gc\n", - "import re\n", - "import time\n", - "\n", - "sys.path.append('../../..')\n", - "\n", - "import pandas as pd\n", - "\n", - "import SimpleITK as sitk\n", - "\n", - "from loguru import logger\n", - "\n", - "# Format the output a bit nicer for Jupyter\n", - "logger.remove()\n", - "logger.add(sys.stdout, format=\"{time:YYYY-MM-DD HH:mm:ss} {level} {message}\", level=\"DEBUG\")\n", - "\n", - "data_path = './data'\n", - "working_path = \"./working\"\n", - "if not os.path.exists(working_path):\n", - " os.makedirs(working_path)\n", - "\n", - "# Read the data into a dictionary\n", - "\n", - "data = {}\n", - "\n", - "for root, dirs, files in os.walk(data_path, topdown=False):\n", - " \n", - " if root == data_path:\n", - " continue\n", - " \n", - " case = root.split('/')[-1]\n", - " data[case] = {}\n", - " for f in files:\n", - " file_path = os.path.join(root, f)\n", - " \n", - " name = f.split('.')[0].upper()\n", - " \n", - " # Clean up names with double underscore:\n", - " name = name.replace('__','_')\n", - " \n", - " observer = None\n", - " \n", - " matches = re.findall(r\"(.*)_([0-9])\", f.split('.')[0])\n", - " \n", - " if len(matches) > 0:\n", - " name = matches[0][0].upper()\n", - " observer = matches[0][1]\n", - " \n", - " if observer: \n", - " if name in data[case]:\n", - " data[case][name][observer] = file_path\n", - " else:\n", - " data[case][name] = {observer: file_path}\n", - " \n", - " else:\n", - " data[case][name] = file_path\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute the interobserver variability for each case" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_inter_ob_var_file = os.path.join(working_path, \"df_inter_ob_var.pkl\")\n", - "\n", - "# If already computed, read the data from a file\n", - "if os.path.exists(df_inter_ob_var_file):\n", - " print(f'Reading from file: {df_inter_ob_var_file}')\n", - " df_inter_ob_var = pd.read_pickle(df_inter_ob_var_file)\n", - "else:\n", - "\n", - " inter_observe_var = []\n", - "\n", - " for c in data:\n", - " for s in data[c]:\n", - " if not s.startswith('STRUCT_'):\n", - " continue\n", - "\n", - " for o1 in data[c][s]:\n", - " for o2 in data[c][s]:\n", - "\n", - " if o1==o2:\n", - " continue\n", - "\n", - " mask_1 = sitk.ReadImage(data[c][s][o1])\n", - " mask_2 = sitk.ReadImage(data[c][s][o2])\n", - "\n", - " lomif = sitk.LabelOverlapMeasuresImageFilter()\n", - " lomif.Execute(mask_1, mask_2)\n", - "\n", - " hdif = sitk.HausdorffDistanceImageFilter()\n", - " hdif.Execute(mask_1, mask_2)\n", - "\n", - " dce = lomif.GetDiceCoefficient()\n", - " hmax = hdif.GetHausdorffDistance()\n", - " havg = hdif.GetAverageHausdorffDistance()\n", - "\n", - " row = {'o1': o1, \n", - " 'o2': o2, \n", - " 'case': c, \n", - " 'struct': s, \n", - " 'dce': dce, \n", - " 'hausdorff_max': hmax, \n", - " 'hausdorff_avg': havg }\n", - "\n", - " inter_observe_var.append(row)\n", - "\n", - " df_inter_ob_var = pd.DataFrame(inter_observe_var)\n", - " print(f'Saving to file: {df_inter_ob_var_file}')\n", - " df_inter_ob_var.to_pickle(df_inter_ob_var_file)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Output the results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_inter = df_inter_ob_var.groupby(['struct']).aggregate(['mean', 'std', 'min', 'max'])\n", - "df_inter = df_inter[['dce','hausdorff_max','hausdorff_avg']]\n", - "df_inter" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/sample/3_registration.ipynb b/examples/sample/3_registration.ipynb deleted file mode 100644 index 05c5d35d..00000000 --- a/examples/sample/3_registration.ipynb +++ /dev/null @@ -1,258 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Atlas Registration\n", - "\n", - "This Notebook will assist with registering every atlas case to every other atlas case. This is useful to conduct a leave-one-out analysis." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "import gc\n", - "import re\n", - "import time\n", - "\n", - "sys.path.append('../../..')\n", - "\n", - "import pandas as pd\n", - "\n", - "import SimpleITK as sitk\n", - "\n", - "from loguru import logger\n", - "\n", - "# Format the output a bit nicer for Jupyter\n", - "logger.remove()\n", - "logger.add(sys.stdout, format=\"{time:YYYY-MM-DD HH:mm:ss} {level} {message}\", level=\"DEBUG\")\n", - "\n", - "from platipy.imaging.registration.registration import initial_registration, fast_symmetric_forces_demons_registration, transform_propagation, apply_field\n", - "\n", - "data_path = './data'\n", - "working_path = \"./working\"\n", - "if not os.path.exists(working_path):\n", - " os.makedirs(working_path)\n", - "\n", - "# Read the data into a dictionary\n", - "\n", - "data = {}\n", - "\n", - "for root, dirs, files in os.walk(data_path, topdown=False):\n", - " \n", - " if root == data_path:\n", - " continue\n", - " \n", - " case = root.split('/')[-1]\n", - " data[case] = {}\n", - " for f in files:\n", - " file_path = os.path.join(root, f)\n", - " \n", - " name = f.split('.')[0].upper()\n", - " \n", - " # Clean up names with double underscore:\n", - " name = name.replace('__','_')\n", - " \n", - " observer = None\n", - " \n", - " matches = re.findall(r\"(.*)_([0-9])\", f.split('.')[0])\n", - " \n", - " if len(matches) > 0:\n", - " name = matches[0][0].upper()\n", - " observer = matches[0][1]\n", - " \n", - " if observer: \n", - " if name in data[case]:\n", - " data[case][name][observer] = file_path\n", - " else:\n", - " data[case][name] = {observer: file_path}\n", - " \n", - " else:\n", - " data[case][name] = file_path\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Register each case to every other case\n", - "\n", - "Used for leave-one-out atlas analysis later" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "# Log to file to avoid large amounts of output in Notebook\n", - "logger.remove()\n", - "logger_handler = logger.add(\"./logs/file_{time}.log\", format=\"{time:YYYY-MM-DD HH:mm:ss} {level} {message}\", level=\"DEBUG\")\n", - "print(\"Logging to file, check 'logs' directory\")\n", - "\n", - "for i in data:\n", - "\n", - " logger.info(f'Will run case: {i}') \n", - " for j in data:\n", - "\n", - " if i == j: continue\n", - "\n", - " start = time.time()\n", - "\n", - " fixed_image = sitk.ReadImage(data[i]['CT'])\n", - " moving_image = sitk.ReadImage(data[j]['CT'])\n", - "\n", - " reg_dir = os.path.join(working_path, f\"{i}_{j}\")\n", - " if not os.path.exists(reg_dir):\n", - " os.makedirs(reg_dir)\n", - "\n", - " # Rigid Reg\n", - " logger.info(f'Rigidly register case {j} (moving) to {i} (fixed)')\n", - " ct_reg_rig, rigid_transform = initial_registration(fixed_image, moving_image)\n", - " ct_reg_rig_path = os.path.join(reg_dir, f'{j}_ct_reg_rig.nii.gz')\n", - " sitk.WriteImage(ct_reg_rig, ct_reg_rig_path)\n", - "\n", - " # Deformable Reg\n", - " logger.info(f'Deformably register case {j} (moving) to {i} (fixed)')\n", - " ct_reg_def, deform_field = fast_symmetric_forces_demons_registration(fixed_image, ct_reg_rig, resolution_staging=[16,4,2,1], iteration_staging=[2,2,2,2], ncores=12)\n", - " ct_reg_def_path = os.path.join(reg_dir, f'{j}_ct_reg_def.nii.gz')\n", - " sitk.WriteImage(ct_reg_def, ct_reg_def_path)\n", - "\n", - " for s in data[j]:\n", - " if not s.upper().startswith('STRUCT'):\n", - " continue\n", - " \n", - " for o in data[j][s]:\n", - " \n", - " logger.info(f'Deforming structure: {s}')\n", - " \n", - " structure_path = data[j][s][o]\n", - "\n", - " structure = sitk.ReadImage(structure_path)\n", - " structure_transformed = transform_propagation(fixed_image, structure, rigid_transform, structure=True)\n", - " structure_deformed = apply_field(structure_transformed, deform_field, structure=True)\n", - " structure_file = os.path.join(reg_dir, structure_path.split('/')[-1].replace('Struct_', f'Struct_def_{j}_'))\n", - " sitk.WriteImage(structure_deformed, structure_file)\n", - "\n", - " end = time.time()\n", - " print(f'Took: {end-start}')\n", - " break\n", - "\n", - "# Revert to logging to output\n", - "logger.remove()\n", - "logger.add(sys.stdout, format=\"{time:YYYY-MM-DD HH:mm:ss} {level} {message}\", level=\"DEBUG\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Read in registered structured to data dictionary" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "for k in data:\n", - " data[k]['moving'] = {} \n", - " \n", - "for root, dirs, files in os.walk(working_path, topdown=False):\n", - " \n", - " parts = re.findall('([0-9]+)_([0-9]+)$',root)\n", - " \n", - " if len(parts) < 1:\n", - " continue\n", - " \n", - " fixed = parts[0][0]\n", - " moving = parts[0][1]\n", - " \n", - " if not moving in data[fixed]['moving'].keys():\n", - " data[fixed]['moving'][moving] = {}\n", - "\n", - " for f in files:\n", - " \n", - " structure_key = f.replace(f'Struct_def_{moving}', 'Struct').split('.')[0]\n", - " \n", - " # Clean up names with double underscore:\n", - " structure_key = structure_key.replace('__','_')\n", - " \n", - " if 'OLD' in structure_key:\n", - " continue\n", - "\n", - " file_path = os.path.join(root, f)\n", - " \n", - " if '_ct_reg_def' in f:\n", - " data[fixed]['moving'][moving]['CT'] = file_path\n", - " \n", - " if not 'Struct' in structure_key:\n", - " continue\n", - " \n", - " name = structure_key.upper()\n", - " observer = None\n", - " \n", - " matches = re.findall(r\"(.*)_([0-9])\", structure_key)\n", - " \n", - " if len(matches) > 0:\n", - " name = matches[0][0].upper()\n", - " observer = matches[0][1]\n", - " \n", - " if observer: \n", - " if name in data[fixed]['moving'][moving]:\n", - " data[fixed]['moving'][moving][name][observer] = file_path\n", - " else:\n", - " data[fixed]['moving'][moving][name] = {observer: file_path}\n", - " \n", - " else:\n", - " data[fixed]['moving'][moving][name] = file_path\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/download/tcia.ipynb b/examples/tcia.ipynb similarity index 100% rename from examples/download/tcia.ipynb rename to examples/tcia.ipynb diff --git a/examples/visualise.ipynb b/examples/visualise.ipynb new file mode 100644 index 00000000..1bdd986e --- /dev/null +++ b/examples/visualise.ipynb @@ -0,0 +1,379 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualise Images\n", + "\n", + "This notebook demonstrates how to display an image along with some contours, scalar overlays and bounding boxes.\n", + "\n", + "## Import Modules\n", + "\n", + "The following cell imports the modules needed for this example. This 'ImageVisualiser' class does most of the heavy lifting regarding visualisation. It has a number of helpful functions, and makes adding additional information to an image (relatively) pain-free." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check if platipy is installed, if not install it.\n", + "try:\n", + " import platipy\n", + "except:\n", + " !pip install git+https://github.com/pyplati/platipy.git\n", + " import platipy\n", + "\n", + "# The ImageVisualiser clas\n", + "from platipy.imaging import ImageVisualiser\n", + "\n", + "# Function to grab some test data\n", + "from platipy.imaging.tests.data import get_lung_nifti\n", + "\n", + "# Usual suspects\n", + "import numpy as np\n", + "import SimpleITK as sitk\n", + "from pathlib import Path\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Some utilities we use for generating interesting data\n", + "from platipy.imaging.registration.utils import convert_mask_to_reg_structure, apply_transform\n", + "from platipy.imaging.registration.deformable import fast_symmetric_forces_demons_registration\n", + "from platipy.imaging.generation.dvf import generate_field_asymmetric_extend\n", + "from platipy.imaging.label.utils import get_com\n", + "from platipy.imaging.utils.crop import label_to_roi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download Test Data\n", + "\n", + "Some Lung test data from LCTSC is fetched here for use in this example notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "input_directory = get_lung_nifti()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read in the data\n", + "\n", + "- ct_image: the x-ray computed tomography scan (3D)\n", + "- contours: a dictionary of binary masks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "pat_directory = input_directory.joinpath(\"LCTSC-Test-S1-101\")\n", + "\n", + "# Read in the CT image\n", + "ct_filename = next(pat_directory.glob(\"**/IMAGES/*.nii.gz\"))\n", + "ct_image = sitk.ReadImage(ct_filename.as_posix())\n", + "\n", + "# Read in the RTStruct contours as binary masks\n", + "contour_filename_list = list(pat_directory.glob(\"**/STRUCTURES/*.nii.gz\"))\n", + "\n", + "contours = {}\n", + "for contour_filename in contour_filename_list:\n", + " _name = contour_filename.name.split(\".nii.gz\")[0].split(\"RTSTRUCT_\")[-1]\n", + " \n", + " contours[_name] = sitk.ReadImage(contour_filename.as_posix())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Display Image with Contours\n", + "\n", + "Here we define 'cut', the location of orthogonal slices\n", + "\n", + "- cut: (\n", + " transaxial slice location *or* first axis,\n", + " coronal slice location *or* second axis,\n", + " saggital slice location *or* third axis,\n", + " )\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "image_visualiser = ImageVisualiser(ct_image, cut=(50,256,256))\n", + "image_visualiser.add_contour(contours)\n", + "fig = image_visualiser.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Display Image without Contours\n", + "\n", + "Here we change the intensity windowing, for example to better visualise lung tissue\n", + "\n", + "- window: (\n", + " minimum intensity,\n", + " range in intensity\n", + " )\n", + " \n", + "It is also possible to use difference colormaps to display the image.\n", + "\n", + "We can also change the field of view, for example using to the lung volumes.\n", + "\n", + "We also don't need to set 'cut', in which case the middle of the image volume is displayed.\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "image_visualiser = ImageVisualiser(ct_image, window=(-900, 1100), colormap=plt.cm.get_cmap(\"terrain\"), figure_size_in=6)\n", + "\n", + "image_visualiser.set_limits_from_label(contours[\"LUNG_L\"] + contours[\"LUNG_R\"])\n", + "\n", + "fig = image_visualiser.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Display Image using Projection\n", + "\n", + "Another way to visualise a 3D image volume is using a projection. Here we demonstrate a mean intensity projection." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "image_visualiser = ImageVisualiser(ct_image, projection=True, window=(0.3,0.8))\n", + "\n", + "image_visualiser.add_bounding_box(label_to_roi(contours[\"HEART\"], return_as_list=True), name=\"HEART BOUNDING BOX\")\n", + "\n", + "fig = image_visualiser.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Display Scalar Field on Image\n", + "\n", + "There are also tools to visualise scalar fields. Here is an example using a normalised distance map, which we compute for the heart volume." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ndm_heart = convert_mask_to_reg_structure(contours[\"HEART\"], expansion=1)\n", + "\n", + "image_visualiser = ImageVisualiser(ct_image, cut=get_com(contours[\"HEART\"]), figure_size_in=6)\n", + "\n", + "image_visualiser.add_scalar_overlay(ndm_heart, name=\"Normalised distance from edge\", discrete_levels=10)\n", + "image_visualiser.add_contour(contours[\"HEART\"], name=\"Heart\", show_legend=True)\n", + "\n", + "image_visualiser.set_limits_from_label(contours[\"HEART\"], expansion=30)\n", + "\n", + "fig = image_visualiser.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overlay Vector Fields on Image\n", + "\n", + "We can also display vector fields. Here we compute one, simulated by expanding the heart volume by 5mm (for this simulation, we also use some nice tools in platipy)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "contours[\"HEART_EXPAND\"], tfm_expansion, dvf_expansion = generate_field_asymmetric_extend(contours[\"HEART\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "image_visualiser = ImageVisualiser(ct_image, cut=get_com(contours[\"HEART\"]), figure_size_in=7)\n", + "\n", + "image_visualiser.add_vector_overlay(\n", + " dvf_expansion,\n", + " name=\"DVF magnitude [mm]\",\n", + " colormap=plt.cm.get_cmap(\"gnuplot2\"),\n", + " alpha=0.75,\n", + " arrow_scale=1,\n", + " arrow_width=1,\n", + " subsample=(2,6,6),\n", + " color_function='magnitude',\n", + " show_colorbar=True\n", + ")\n", + "\n", + "image_visualiser.add_contour(contours[\"HEART_EXPAND\"], name = \"Heart (expanded)\", color=\"#880088\", linewidth=5)\n", + "image_visualiser.add_contour(contours[\"HEART\"], name = \"Heart (original)\", color=\"#88FF88\", linewidth=5)\n", + "\n", + "image_visualiser.set_limits_from_label(contours[\"HEART_EXPAND\"], expansion=40)\n", + "\n", + "fig = image_visualiser.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compare two Images\n", + "\n", + "We can also visualise comparisons between images. This can be helpful for checking registration performance visually.\n", + "\n", + "In this example, we deform the ct_image using the transformation above (heart expansion).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ct_image_deformed = apply_transform(\n", + " input_image=ct_image,\n", + " reference_image=None,\n", + " transform=tfm_expansion,\n", + " default_value=-1000,\n", + " interpolator=sitk.sitkBSpline,\n", + ")\n", + "\n", + "image_visualiser = ImageVisualiser(ct_image, cut=get_com(contours[\"HEART\"]), figure_size_in=7, window=(-40, 200))\n", + "\n", + "image_visualiser.add_comparison_overlay(ct_image_deformed)\n", + "\n", + "image_visualiser.set_limits_from_label(contours[\"HEART_EXPAND\"], expansion=40)\n", + "\n", + "fig = image_visualiser.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Combine Visualisations\n", + "\n", + "For experienced matplotlib users, it is also possible to customise the figure after it has been created. We can add test, more axes, etc. as usual..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "image_visualiser = ImageVisualiser(ct_image, cut=get_com(contours[\"HEART\"]), figure_size_in=9)\n", + "\n", + "image_visualiser.add_scalar_overlay(ndm_heart, name=\"Normalised distance from edge\", discrete_levels=10)\n", + "\n", + "image_visualiser.add_contour(contours[\"HEART_EXPAND\"], name = \"Heart (expanded)\", color=\"#880088\", linewidth=5)\n", + "image_visualiser.add_contour(contours[\"HEART\"], name = \"Heart (original)\", color=\"#88FF88\", linewidth=5)\n", + "\n", + "image_visualiser.add_vector_overlay(\n", + " dvf_expansion,\n", + " name=\"DVF magnitude [mm]\",\n", + " colormap=plt.cm.get_cmap(\"gnuplot2\"),\n", + " alpha=0.5,\n", + " arrow_scale=1,\n", + " arrow_width=1,\n", + " subsample=(2,6,6),\n", + " color_function='magnitude',\n", + " show_colorbar=True\n", + ")\n", + "\n", + "image_visualiser.set_limits_from_label(contours[\"HEART_EXPAND\"], expansion=40)\n", + "\n", + "fig = image_visualiser.show()\n", + "\n", + "# Here we can add stuff to the figure\n", + "\n", + "fig.text(0.99, 0.99, \"TEST VISUALISATION\", va=\"top\", ha=\"right\", fontsize=14)\n", + "fig.text(0.99, 0.96, \"using platipy\", va=\"top\", ha=\"right\", fontsize=12, style=\"italic\", c=\"red\");\n", + "\n", + "test_axis = fig.add_axes( (0.85, 0.55, 0.1, 0.1) )\n", + "test_axis.hist(np.random.random(1000), histtype=\"stepfilled\", fc = \"#1144EE\", ec=None)\n", + "test_axis.set_xlabel(\"Results\")\n", + "\n", + "# Optionally, save the figure\n", + "# fig.savefig(\"./output.jpeg\", dpi=500)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/platipy/cli/nifti_to_rtstruct.py b/platipy/cli/nifti_to_rtstruct.py index 6b65ea44..93f28557 100644 --- a/platipy/cli/nifti_to_rtstruct.py +++ b/platipy/cli/nifti_to_rtstruct.py @@ -14,7 +14,7 @@ import click -from platipy.dicom.nifti_to_rtstruct.convert import convert_nifti +from platipy.dicom.io.nifti_to_rtstruct import convert_nifti @click.command() @@ -24,22 +24,14 @@ required=True, help="Reference DICOM file from which header tags will be copied", ) -@click.option( - "--debug", - default=False, - is_flag=True, - help="Whether intermediate debug info is written", -) -@click.option( - "--mask", "-m", multiple=True, required=True, help="Mask pairs with name,filename" -) +@click.option("--mask", "-m", multiple=True, required=True, help="Mask pairs with name,filename") @click.option("--out_rt_filename", "-o", required=True, help="Name of RT struct output") -def click_command(dcm_file, debug, mask, out_rt_filename): +def click_command(dcm_file, mask, out_rt_filename): """ Convert Nifti masks to Dicom RTStruct """ - convert_nifti(dcm_file, mask, out_rt_filename, debug=debug) + convert_nifti(dcm_file, mask, out_rt_filename) if __name__ == "__main__": diff --git a/platipy/cli/nifti_to_series.py b/platipy/cli/nifti_to_series.py index e5f6d2b6..9fdcd5bb 100755 --- a/platipy/cli/nifti_to_series.py +++ b/platipy/cli/nifti_to_series.py @@ -17,7 +17,7 @@ import SimpleITK as sitk -from platipy.dicom.nifti_to_series.convert import convert_nifti_to_dicom_series +from platipy.dicom.io.nifti_to_series import convert_nifti_to_dicom_series @click.command() diff --git a/platipy/cli/rtstruct_to_nifti.py b/platipy/cli/rtstruct_to_nifti.py index a14d4f9e..4ac6bafb 100644 --- a/platipy/cli/rtstruct_to_nifti.py +++ b/platipy/cli/rtstruct_to_nifti.py @@ -15,7 +15,8 @@ import click -from platipy.dicom.rtstruct_to_nifti.convert import convert_rtstruct +from platipy.dicom.io.rtstruct_to_nifti import convert_rtstruct + @click.command() @click.option( @@ -30,15 +31,13 @@ required=True, help="Dicom RTStruct file containing the contours to convert", ) -@click.option( - "--prefix", "-p", default="Struct_", help="Prefix for output files (e.g. Case_01_" -) +@click.option("--prefix", "-p", default="Struct_", help="Prefix for output files (e.g. Case_01_") @click.option( "--output_dir", "-od", default=".", help="Directory in which to place converted files", - show_default=True + show_default=True, ) @click.option("--output_img", "-oi", help="Output name of converted image series") @click.option( diff --git a/platipy/dicom/download/tcia.py b/platipy/dicom/download/tcia.py index b32f79d1..c5a92574 100644 --- a/platipy/dicom/download/tcia.py +++ b/platipy/dicom/download/tcia.py @@ -21,7 +21,9 @@ import requests from loguru import logger -from platipy.dicom.dicom_directory_crawler.conversion_utils import process_dicom_directory +from platipy.dicom.io.crawl import ( + process_dicom_directory, +) API_URL = "https://services.cancerimagingarchive.net/services/v4/TCIA" diff --git a/platipy/dicom/tests/test_crawl.py b/platipy/dicom/tests/test_crawl.py new file mode 100644 index 00000000..bb0c3907 --- /dev/null +++ b/platipy/dicom/tests/test_crawl.py @@ -0,0 +1,148 @@ +# Copyright 2020 University of New South Wales, University of Sydney, Ingham Institute + +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# pylint: disable=redefined-outer-name,missing-function-docstring + +import tempfile +from pathlib import Path + +import pytest +import pydicom +import numpy as np +import SimpleITK as sitk + +from platipy.imaging.tests.data import get_lung_dicom, get_lung_nifti +from platipy.dicom.io.rtstruct_to_nifti import convert_rtstruct +from platipy.dicom.io.nifti_to_rtstruct import convert_nifti + + +@pytest.fixture +def dicom_data(): + """Gets the data needed for converting DICOM + + Returns: + pathlib.Path: Path to DICOM data + """ + return get_lung_dicom() + + +@pytest.fixture +def nifti_data(): + """Gets the data needed for converting NIFTI + + Returns: + pathlib.Path: Path to NIFTI data + """ + return get_lung_nifti() + + +def test_convert_dicom_to_nifti(dicom_data): + + test_pat_id = "LCTSC-Test-S1-101" + ct_uid = "1.3.6.1.4.1.14519.5.2.1.7014.4598.106943890850011666503487579262" + rtstruct_uid = "1.3.6.1.4.1.14519.5.2.1.7014.4598.280355341349691222365783556597" + + pat_path = dicom_data.joinpath(test_pat_id) + ct_path = pat_path.joinpath(ct_uid) + rtstruct_path = next(pat_path.joinpath(rtstruct_uid).glob("*.dcm")) + + pre = "Test_" + + output_img = "img.nii.gz" + with tempfile.TemporaryDirectory() as temp_dir: + output_dir = Path(temp_dir) + + # Run the function + convert_rtstruct( + ct_path, rtstruct_path, prefix=pre, output_dir=output_dir, output_img=output_img + ) + + # Check some of the output files for sanity + assert len(list(output_dir.glob("*.nii.gz"))) == 6 + + # Check the converted image series + ct_output = next(output_dir.glob("img.nii.gz")) + im = sitk.ReadImage(str(ct_output)) + + assert np.allclose(im.GetOrigin(), (-249.51, -483.01, -640.2), atol=0.01) + assert np.allclose(im.GetSize(), (512, 512, 130), atol=0.01) + assert np.allclose(im.GetSpacing(), (0.97, 0.97, 3.0), atol=0.01) + + nda = sitk.GetArrayFromImage(im) + assert nda.sum() == -23952778432 + + # Check a converted contour mask + heart_output = next(output_dir.glob("Test_Heart.nii.gz")) + mask = sitk.ReadImage(str(heart_output)) + assert np.allclose(mask.GetOrigin(), (-249.51, -483.01, -640.2), atol=0.01) + assert np.allclose(mask.GetSize(), (512, 512, 130), atol=0.01) + assert np.allclose(mask.GetSpacing(), (0.97, 0.97, 3.0), atol=0.01) + nda = sitk.GetArrayFromImage(mask) + + assert nda.sum() == 261048 + + +def test_convert_nifti_to_dicom(nifti_data, dicom_data): + + test_pat_id = "LCTSC-Test-S1-101" + ct_uid = "1.3.6.1.4.1.14519.5.2.1.7014.4598.106943890850011666503487579262" + rtstruct_uid = "1.3.6.1.4.1.14519.5.2.1.7014.4598.280355341349691222365783556597" + + pat_path = dicom_data.joinpath(test_pat_id) + ct_path = pat_path.joinpath(ct_uid) + rtstruct_path = next(pat_path.joinpath(rtstruct_uid).glob("*.dcm")) + + # Generate dict of masks in the masks directory + masks = {} + for mask_path in nifti_data.joinpath(test_pat_id).glob("STRUCTURES/*.nii.gz"): + name = mask_path.name.split(".")[0].split("RTSTRUCT_")[1] + masks[name] = str(mask_path) + + with tempfile.TemporaryDirectory() as temp_dir: + + output_file = Path(temp_dir).joinpath("test.dcm") + + # Run the function + convert_nifti(ct_path, masks, output_file) + + # Check some of the output files for sanity + original = pydicom.read_file(rtstruct_path) + rts = pydicom.read_file(output_file) + + assert rts.Modality == original.Modality + assert rts.PatientName == original.PatientName + assert rts.PatientID == original.PatientID + + # Check that all the contour names match + contour_map = {} + for i in original.StructureSetROISequence: + for j in rts.StructureSetROISequence: + if j.ROIName == i.ROIName: + contour_map[int(i.ROINumber)] = int(j.ROINumber) + assert len(contour_map.keys()) == 11 + + # Confirm that the min/max contour points land within 1 voxel size of the original + i = 1 + for j in original.ROIContourSequence[i - 1].ContourSequence: + + for k in rts.ROIContourSequence[contour_map[i] - 1].ContourSequence: + + if j.ContourData[2] == k.ContourData[2]: + j_points = np.array(j.ContourData) + j_points = j_points.reshape(j.NumberOfContourPoints, 3) + + k_points = np.array(k.ContourData) + k_points = k_points.reshape(k.NumberOfContourPoints, 3) + assert (max(abs(k_points.min(axis=0) - j_points.min(axis=0)))) < 0.83 + assert (max(abs(k_points.max(axis=0) - j_points.max(axis=0)))) < 0.83 diff --git a/platipy/imaging/tests/test_visualiser.py b/platipy/imaging/tests/test_visualiser.py index 88349f27..fd3e891f 100644 --- a/platipy/imaging/tests/test_visualiser.py +++ b/platipy/imaging/tests/test_visualiser.py @@ -12,18 +12,20 @@ # See the License for the specific language governing permissions and # limitations under the License. -# pylint: disable=redefined-outer-name +# pylint: disable=redefined-outer-name,missing-function-docstring import tempfile import pytest import SimpleITK as sitk +import numpy as np from platipy.imaging.tests.data import get_lung_nifti from platipy.imaging import ImageVisualiser from platipy.imaging.label.utils import get_com +from platipy.imaging.visualisation.animation import generate_animation_from_image_sequence @pytest.fixture @@ -45,10 +47,70 @@ def test_contour_visualisation(nifti_data): img = sitk.ReadImage(str(ct_path)) vis = ImageVisualiser(img, cut=get_com(structures["HEART"])) - vis.add_contour(structures) + vis.add_contour(structures) # Add the contours fig = vis.show() assert len(fig.axes[0].collections) == 5 # Check correct number of contours for idx, structure in enumerate(structures.keys()): assert fig.axes[0].legend().get_texts()[idx].get_text() == structure + + +def test_scalar_overlay_visualisation(nifti_data): + + patient_path = nifti_data.joinpath("LCTSC-Test-S1-201") + + ct_path = next(patient_path.glob("IMAGES/*.nii.gz")) + + structures = { + struct.name.split(".nii.gz")[0].split("RTSTRUCT_")[-1]: sitk.ReadImage(str(struct)) + for struct in patient_path.glob("STRUCTURES/*.nii.gz") + } + img = sitk.ReadImage(str(ct_path)) + + vis = ImageVisualiser(img, cut=get_com(structures["HEART"])) + vis.add_scalar_overlay(structures["HEART"] * 5) # Add a dummy overlay + fig = vis.show() + + assert len(fig.axes[0].images) == 2 # Check correct number of images + img = fig.axes[0].images[-1] + assert img.get_array().data.sum() == 61295 + + +def test_comparison_overlay_visualisation(nifti_data): + + patient_path = nifti_data.joinpath("LCTSC-Test-S1-201") + + ct_path = next(patient_path.glob("IMAGES/*.nii.gz")) + + structures = { + struct.name.split(".nii.gz")[0].split("RTSTRUCT_")[-1]: sitk.ReadImage(str(struct)) + for struct in patient_path.glob("STRUCTURES/*.nii.gz") + } + img = sitk.ReadImage(str(ct_path)) + + vis = ImageVisualiser(img, cut=get_com(structures["HEART"])) + vis.add_comparison_overlay(structures["HEART"] * 5) # Add a dummy overlay + fig = vis.show() + + assert len(fig.axes[0].images) == 1 # Check correct number of images + img = fig.axes[0].images[0] + print(img.get_array().data.sum()) + assert np.allclose(img.get_array().data.sum(), 207349, atol=1) + + +def test_animation_visualisation(nifti_data): + + patient_path = nifti_data.joinpath("LCTSC-Test-S1-201") + + ct_path = next(patient_path.glob("IMAGES/*.nii.gz")) + + img = sitk.ReadImage(str(ct_path)) + + imlist = [img[:, :, x] for x in range(0, img.GetSize()[2], 10)] + with tempfile.NamedTemporaryFile("w", suffix=".gif") as output_file: + generate_animation_from_image_sequence(imlist, output_file=output_file.name) + + # Check that a GIF file has been generated + with open(output_file.name, "rb") as f: + assert f.read(6).decode("utf-8") in ("GIF87a", "GIF89a") diff --git a/platipy/imaging/visualisation/animation.py b/platipy/imaging/visualisation/animation.py index f7e28ff7..965556ea 100644 --- a/platipy/imaging/visualisation/animation.py +++ b/platipy/imaging/visualisation/animation.py @@ -12,46 +12,82 @@ # See the License for the specific language governing permissions and # limitations under the License. -from matplotlib import rcParams - +import tempfile import pathlib +import shutil + +import imageio import numpy as np import SimpleITK as sitk import matplotlib.pyplot as plt -import matplotlib.gridspec as gridspec import matplotlib.animation as animation +from matplotlib.animation import FileMovieWriter + + +class FileWriter(FileMovieWriter): + """Class to write image frames for animation""" + + supported_formats = ["png"] + + # pylint: disable=arguments-differ, attribute-defined-outside-init + def setup(self, fig, dpi, frame_prefix): + super().setup(fig, dpi, frame_prefix) + self.fname_format_str = "%s%%d.%s" + self.temp_prefix, self.frame_format = self.outfile.split(".") + + def grab_frame(self, **savefig_kwargs): + + with self._frame_sink() as myframesink: + self.fig.savefig(myframesink, format="png", dpi=self.dpi, **savefig_kwargs) -from platipy.imaging.visualisation.utils import project_onto_arbitrary_plane + def finish(self): + self._frame_sink().close() def generate_animation_from_image_sequence( image_list, output_file="animation.gif", fps=10, - contour_list=False, - scalar_list=False, + contour_list=None, + scalar_list=None, figure_size_in=6, - image_cmap=plt.cm.Greys_r, - contour_cmap=plt.cm.jet, - scalar_cmap=plt.cm.magma, + image_cmap=plt.cm.get_cmap("Greys_r"), + contour_cmap=plt.cm.get_cmap("jet"), + scalar_cmap=plt.cm.get_cmap("magma"), image_window=[-1000, 800], - scalar_min=False, - scalar_max=False, + scalar_min=None, + scalar_max=None, scalar_alpha=0.5, image_origin="lower", ): - - # We need to check for ImageMagick - # There may be other tools that can be used - rcParams["animation.convert_path"] = r"/usr/bin/convert" - convert_path = pathlib.Path(rcParams["animation.convert_path"]) - - if not convert_path.exists(): - raise RuntimeError("To use this function you need ImageMagick.") - - if type(image_list[0]) is not sitk.Image: + """Make a GIF animation from the list of images supplied + + Args: + image_list (list): List of 2D sitk.Image's to render + output_file (str, optional): Path to output GIF file. Defaults to "animation.gif". + fps (int, optional): Frames per second. Defaults to 10. + contour_list (list, optional): List of contours to overlay. Defaults to None. + scalar_list (list, optional): List of scalars to overlay. Defaults to None. + figure_size_in (int, optional): Size of figure. Defaults to 6. + image_cmap ([type], optional): Colormap for image. Defaults to plt.cm.get_cmap("Greys_r"). + contour_cmap ([type], optional): Colormap for contours. Defaults to plt.cm.get_cmap("jet"). + scalar_cmap ([type], optional): Colormap for scalars. Defaults to plt.cm.get_cmap("magma"). + image_window (list, optional): Window for image. Defaults to [-1000, 800]. + scalar_min (float, optional): Minimum value for scalar. Defaults to None. + scalar_max (float, optional): Maximum value for scalar. Defaults to None. + scalar_alpha (float, optional): Alpha value for scalar. Defaults to 0.5. + image_origin (str, optional): Origin of image. Defaults to "lower". + + Raises: + ValueError: Raised if image list does not contain sitk.Image's + + Returns: + matplotlib.animation.FuncAnimation: The animation object + """ + + if not isinstance(image_list[0], sitk.Image): raise ValueError("Each image must be a SimplITK image (sitk.Image).") # Get the image information @@ -77,9 +113,9 @@ def generate_animation_from_image_sequence( # We now deal with the contours # These can be given as a list of sitk.Image objects or a list of dicts {"name":sitk.Image} - if contour_list is not False: + if contour_list: - if type(contour_list[0]) is not dict: + if isinstance(contour_list[0], sitk.Image): plot_dict = {"_": contour_list[0]} contour_labels = False else: @@ -107,11 +143,11 @@ def generate_animation_from_image_sequence( fontsize=min([10, 16 * approx_scaling]), ) - if scalar_list is not False: + if scalar_list: - if scalar_min is False: + if not scalar_min: scalar_min = np.min([sitk.GetArrayFromImage(i) for i in scalar_list]) - if scalar_max is False: + if not scalar_max: scalar_max = np.max([sitk.GetArrayFromImage(i) for i in scalar_list]) display_scalar = ax.imshow( @@ -137,13 +173,13 @@ def animate(i): display_image.set_data(nda) # TO DO - add in code for scalar overlay - if contour_list is not False: + if contour_list: try: ax.collections = [] except ValueError: pass - if type(contour_list[i]) is not dict: + if not isinstance(contour_list[i], dict): plot_dict = {"_": contour_list[i]} else: plot_dict = contour_list[i] @@ -152,21 +188,21 @@ def animate(i): for index, contour in enumerate(plot_dict.values()): - display_contours = ax.contour( + ax.contour( sitk.GetArrayFromImage(contour), colors=[color_map[index]], levels=[0], linewidths=2, ) - if scalar_list is not False: + if scalar_list: nda = sitk.GetArrayFromImage(scalar_list[i]) display_scalar.set_data(np.ma.masked_outside(nda, scalar_min, scalar_max)) return (display_image,) # create animation using the animate() function with no repeat - myAnimation = animation.FuncAnimation( + animation_result = animation.FuncAnimation( fig, animate, frames=np.arange(0, len(image_list), 1), @@ -175,7 +211,23 @@ def animate(i): repeat=False, ) - # save animation at 30 frames per second - myAnimation.save(output_file, writer="imagemagick", fps=fps) + # Save animation + tmp_path = tempfile.mkdtemp() + animation_result.save(f"{tmp_path}/tmp.format", writer=FileWriter()) + + # Save the GIF + images = [] + for filename in pathlib.Path(tmp_path).glob("tmp*.png"): + + try: + images.append(imageio.imread(filename)) + except RuntimeError: + # Skip frames which are corrupt + pass + + imageio.mimsave(output_file, images, fps=fps) + + # Clean up + shutil.rmtree(tmp_path) - return myAnimation \ No newline at end of file + return animation_result diff --git a/platipy/imaging/visualisation/utils.py b/platipy/imaging/visualisation/utils.py index 92dd4aa2..6b3f5543 100644 --- a/platipy/imaging/visualisation/utils.py +++ b/platipy/imaging/visualisation/utils.py @@ -17,6 +17,99 @@ import numpy as np import SimpleITK as sitk +import matplotlib.pyplot as plt + +from platipy.imaging.utils.crop import label_to_roi + + +class VisualiseContour: + """Class to represent the visualiation of a contour""" + + def __init__(self, image, name, color=None, linewidth=2): + self.image = image + self.name = name + self.color = color + self.linewidth = linewidth + + +class VisualiseScalarOverlay: + """Class to represent the visualiation of a scalar overlay""" + + def __init__( + self, + image, + name, + colormap=plt.cm.get_cmap("Spectral"), + alpha=0.75, + min_value=False, + max_value=False, + discrete_levels=False, + mid_ticks=False, + show_colorbar=True, + norm=None, + ): + self.image = image + self.name = name + self.colormap = colormap + self.alpha = alpha + self.min_value = min_value + self.max_value = max_value + self.discrete_levels = discrete_levels + self.mid_ticks = mid_ticks + self.show_colorbar = show_colorbar + self.norm = norm + + +class VisualiseVectorOverlay: + """Class to represent the visualiation of a vector overlay""" + + def __init__( + self, + image, + name, + colormap=plt.cm.get_cmap("Spectral"), + alpha=0.75, + arrow_scale=0.25, + arrow_width=1, + subsample=4, + color_function="perpendicular", + invert_field=True, + show_colorbar=True, + ): + self.image = image + self.name = name + self.colormap = colormap + self.alpha = alpha + self.arrow_scale = arrow_scale + self.arrow_width = arrow_width + self.subsample = subsample + self.color_function = color_function + self.invert_field = invert_field + self.show_colorbar = show_colorbar + + +class VisualiseComparisonOverlay: + """Class to represent the visualiation of a comparison image""" + + def __init__(self, image, name, color_rotation=0.35): + self.image = image + self.name = name + self.color_rotation = color_rotation + + +class VisualiseBoundingBox: + """Class to represent the visualiation of a bounding box""" + + def __init__(self, bounding_box, name, color="r", linewidth=2): + + if isinstance(bounding_box, sitk.Image): + bounding_box = label_to_roi(bounding_box, return_as_list=True) + + self.bounding_box = bounding_box + self.name = name + self.color = color + self.linewidth = linewidth + def return_slice(axis, index): """Prepares a slice tuple to use for extracting a slice for rendering diff --git a/platipy/imaging/visualisation/visualiser.py b/platipy/imaging/visualisation/visualiser.py index a5dfdc55..fc92852f 100644 --- a/platipy/imaging/visualisation/visualiser.py +++ b/platipy/imaging/visualisation/visualiser.py @@ -23,10 +23,14 @@ from loguru import logger -# from ipywidgets import interact -# import ipywidgets as widgets +from platipy.imaging.utils.crop import label_to_roi from platipy.imaging.visualisation.utils import ( + VisualiseBoundingBox, + VisualiseContour, + VisualiseScalarOverlay, + VisualiseVectorOverlay, + VisualiseComparisonOverlay, return_slice, subsample_vector_field, vector_image_grid, @@ -35,9 +39,6 @@ project_onto_arbitrary_plane, ) -from platipy.imaging.utils.crop import label_to_roi - - """ This Python script comprises two contributions to the code base: 1) A bunch of helpful visualisation "helper" functions @@ -46,95 +47,6 @@ """ -class VisualiseContour: - """Class to represent the visualiation of a contour""" - - def __init__(self, image, name, color=None, linewidth=2): - self.image = image - self.name = name - self.color = color - self.linewidth = linewidth - - -class VisualiseScalarOverlay: - """Class to represent the visualiation of a scalar overlay""" - - def __init__( - self, - image, - name, - colormap=plt.cm.get_cmap("Spectral"), - alpha=0.75, - min_value=False, - max_value=False, - discrete_levels=False, - mid_ticks=False, - show_colorbar=True, - norm=None, - ): - self.image = image - self.name = name - self.colormap = colormap - self.alpha = alpha - self.min_value = min_value - self.max_value = max_value - self.discrete_levels = discrete_levels - self.mid_ticks = mid_ticks - self.show_colorbar = show_colorbar - self.norm = norm - - -class VisualiseVectorOverlay: - """Class to represent the visualiation of a vector overlay""" - - def __init__( - self, - image, - name, - colormap=plt.cm.get_cmap("Spectral"), - alpha=0.75, - arrow_scale=0.25, - arrow_width=1, - subsample=4, - color_function="perpendicular", - invert_field=True, - show_colorbar=True, - ): - self.image = image - self.name = name - self.colormap = colormap - self.alpha = alpha - self.arrow_scale = arrow_scale - self.arrow_width = arrow_width - self.subsample = subsample - self.color_function = color_function - self.invert_field = invert_field - self.show_colorbar = show_colorbar - - -class VisualiseComparisonOverlay: - """Class to represent the visualiation of a comparison image""" - - def __init__(self, image, name, color_rotation=0.35): - self.image = image - self.name = name - self.color_rotation = color_rotation - - -class VisualiseBoundingBox: - """Class to represent the visualiation of a bounding box""" - - def __init__(self, bounding_box, name, color="r", linewidth=2): - - if isinstance(bounding_box, sitk.Image): - bounding_box = label_to_roi(bounding_box, return_as_list=True) - - self.bounding_box = bounding_box - self.name = name - self.color = color - self.linewidth = linewidth - - class ImageVisualiser: """Class to assist with visualising images and overlaying contours, scalars and bounding boxes.""" @@ -170,7 +82,7 @@ def __init__( self.__projection = projection self.__image_view = None self.__scalar_view = None - self.__contour_color_base = None + self.__contour_colormap = None self.clear() @@ -230,7 +142,7 @@ def add_contour( contour, name=None, color=None, - colorbase=plt.cm.get_cmap("rainbow"), + colormap=plt.cm.get_cmap("rainbow"), linewidth=2, show_legend=True, ): @@ -277,11 +189,7 @@ def add_contour( # Use a default name if not specified if name is None: - name = "input" - self.__show_legend = False - - else: - self.__show_legend = True + name = "contour" visualise_contour = VisualiseContour(contour, name, color=color, linewidth=linewidth) self.__contours.append(visualise_contour) @@ -292,7 +200,7 @@ def add_contour( "and sitk.Image as value, or as an sitk.Image and passing the contour_name" ) - self.__contour_color_base = colorbase + self.__contour_colormap = colormap def add_scalar_overlay( self, @@ -512,127 +420,27 @@ def add_bounding_box(self, bounding_box, name=None, color="r", linewidth=2): def show(self, interact=False): """Render the image with all overlays""" if len(self.__comparison_overlays) == 0: - self.display_slice() + self._display_slice() else: - self.overlay_comparison() + self._overlay_comparison() - self.overlay_scalar_field() - self.overlay_vector_field() - self.overlay_contours() - self.overlay_bounding_boxes() + self._overlay_scalar_field() + self._overlay_vector_field() + self._overlay_contours() + self._overlay_bounding_boxes() - self.adjust_view() + self._adjust_view() if interact: logger.warning("Interactive mode not yet implemented") # self.interact_adjust_slice() self.__figure.canvas.draw() - self.add_legend() + self._add_legend() return self.__figure - # def precompute_array_slices(self): - # pass - - # def interact_adjust_slice(self): - # image = self.__image - # nda = sitk.GetArrayViewFromImage(image) - # (ax_size, cor_size, sag_size) = nda.shape[:3] - - # image_view = self.__image_view - - # if hasattr(self, "__scalar_view"): - # use_scalar = True - # scalar_view = self.__scalar_view - # scalar_image = self.__scalar_overlays[0] # TO DO - generalsie - # nda_scalar = sitk.GetArrayFromImage(scalar_image) - # else: - # use_scalar = False - - # # ~10x speed-up by pre-contructing views - # arr_slices_ax = {i: nda.__getitem__(return_slice("z", i)) for i in range(ax_size)} - # arr_slices_cor = {i: nda.__getitem__(return_slice("y", i)) for i in range(cor_size)} - # arr_slices_sag = {i: nda.__getitem__(return_slice("x", i)) for i in range(sag_size)} - - # if use_scalar: - # scalar_arr_slices_ax = { - # i: nda_scalar.__getitem__(return_slice("z", i)) for i in range(ax_size) - # } - # scalar_arr_slices_cor = { - # i: nda_scalar.__getitem__(return_slice("y", i)) for i in range(cor_size) - # } - # scalar_arr_slices_sag = { - # i: nda_scalar.__getitem__(return_slice("x", i)) for i in range(sag_size) - # } - - # if self.__cut is None: - # slice_ax = int(ax_size / 2.0) - # slice_cor = int(cor_size / 2.0) - # slice_sag = int(sag_size / 2.0) - - # self.__cut = [slice_ax, slice_cor, slice_sag] - - # for view_name in image_view.keys(): - # if view_name == "ax_view": - - # ax_view_image = image_view["ax_view"] - # if use_scalar: - # ax_view_scalar = scalar_view["ax_view"] - - # widget = widgets.IntSlider(min=0, max=ax_size, step=1, value=self.__cut[0]) - - # def f_adjust(axial_slice): - # ax_view_image.set_data(image_arr_slices_ax[axial_slice]) - # if use_scalar: - # ax_view_scalar.set_data(scalar_arr_slices_ax[axial_slice]) - # return - - # interact(f_adjust, axial_slice=widget) - - # if view_name == "cor_view": - - # cor_view_image = image_view["cor_view"] - # if use_scalar: - # cor_view_scalar = scalar_view["cor_view"] - - # if self.__axis == "y" or self.__axis == "cor": - # coronal_cut_default = self.__cut - # else: - # coronal_cut_default = self.__cut[1] - - # widget = widgets.IntSlider(min=0, max=cor_size, step=1, value=self.__cut[1]) - - # def f_adjust(coronal_slice): - # cor_view_image.set_data(image_arr_slices_cor[coronal_slice]) - # if use_scalar: - # cor_view_scalar.set_data(scalar_arr_slices_cor[coronal_slice]) - # return - - # interact(f_adjust, coronal_slice=widget) - - # if view_name == "sag_view": - - # sag_view_image = image_view["sag_view"] - # if use_scalar: - # sag_view_scalar = scalar_view["sag_view"] - - # if self.__axis == "x" or self.__axis == "sag": - # sagittal_cut_default = self.__cut - # else: - # sagittal_cut_default = self.__cut[2] - - # widget = widgets.IntSlider(min=0, max=sag_size, step=1, value=self.__cut[2]) - - # def f_adjust(sagittal_slice): - # sag_view_image.set_data(image_arr_slices_sag[sagittal_slice]) - # if use_scalar: - # sag_view_scalar.set_data(scalar_arr_slices_sag[sagittal_slice]) - # return - - # interact(f_adjust, sagittal_slice=widget) - - def display_slice(self): + def _display_slice(self): """Display the configured image slice""" image = self.__image @@ -816,7 +624,7 @@ def display_slice(self): self.__image_view = {axis_view_name_consistent: ax_indiv} - def overlay_comparison(self): + def _overlay_comparison(self): """Display an overlay comparison Args: @@ -976,7 +784,7 @@ def overlay_comparison(self): self.__figure.subplots_adjust(left=0, right=1, bottom=0, top=1) - def adjust_view(self): + def _adjust_view(self): """adjust_view is a helper function for modifying axis limits. Specify *limits* when initialising the ImageVisulaiser to use. Alternatively, use set_limits_from_label to specify automatically. @@ -1090,7 +898,7 @@ def adjust_view(self): self.__figure.set_size_inches(fig_size_x, fig_size_y) - def overlay_contours(self): + def _overlay_contours(self): """Overlay the contours on to the current figure image""" if len(self.__contours) == 0: @@ -1108,7 +916,7 @@ def overlay_contours(self): if contour.color is not None: color_dict[contour.name] = contour.color else: - color_map = self.__contour_color_base(np.linspace(0, 1, len(self.__contours))) + color_map = self.__contour_colormap(np.linspace(0, 1, len(self.__contours))) color_dict[contour.name] = color_map[color_gen_index % 255] color_gen_index += 1 @@ -1217,7 +1025,7 @@ def overlay_contours(self): else: raise ValueError('Axis is must be one of "x","y","z","ortho".') - def overlay_scalar_field(self): + def _overlay_scalar_field(self): """Overlay the scalar image onto the existing figure""" if self.__projection and len(self.__scalar_overlays) > 0: @@ -1401,7 +1209,7 @@ def overlay_scalar_field(self): "sag_view": sag_view, } - def overlay_vector_field(self): + def _overlay_vector_field(self): """Overlay vector field onto existing figure""" if self.__projection and len(self.__vector_overlays) > 0: @@ -1562,7 +1370,38 @@ def overlay_vector_field(self): cbar = self.__figure.colorbar(sp_vector, cax=cax, orientation="vertical") cbar.set_label(vector.name) - def overlay_bounding_boxes(self): + def draw_bounding_box_on_axes(self, ax, view, box, add_label=True): + + sag_0, cor_0, ax_0, sag_d, cor_d, ax_d = box.bounding_box + + from_points = None + to_points = None + + if view == "z" or view == "ax": + from_points = [sag_0, sag_0, sag_0 + sag_d, sag_0 + sag_d, sag_0] + to_points = [cor_0, cor_0 + cor_d, cor_0 + cor_d, cor_0, cor_0] + + if view == "y" or view == "cor": + from_points = [sag_0, sag_0 + sag_d, sag_0 + sag_d, sag_0, sag_0] + to_points = [ax_0, ax_0, ax_0 + ax_d, ax_0 + ax_d, ax_0] + + if view == "x" or view == "sag": + from_points = [cor_0, cor_0 + cor_d, cor_0 + cor_d, cor_0, cor_0] + to_points = [ax_0, ax_0, ax_0 + ax_d, ax_0 + ax_d, ax_0] + + if from_points is None: + logger.error("Invalid View") + return + + ax.plot( + from_points, + to_points, + lw=box.linewidth, + c=box.color, + label=box.name if add_label else None, + ) + + def _overlay_bounding_boxes(self): """Overlay bounding boxes onto existing figure Args: @@ -1570,64 +1409,20 @@ def overlay_bounding_boxes(self): """ for box in self.__bounding_boxes: - sag_0, cor_0, ax_0, sag_d, cor_d, ax_d = box.bounding_box - - if box.color: - color = box.color - linewidth = box.linewidth # Test types of axes axes = self.__figure.axes[:4] if len(axes) < 4: - ax = axes[0] - - if self.__axis == "z" or self.__axis == "ax": - ax.plot( - [sag_0, sag_0, sag_0 + sag_d, sag_0 + sag_d, sag_0], - [cor_0, cor_0 + cor_d, cor_0 + cor_d, cor_0, cor_0], - lw=linewidth, - c=color, - label=box.name, - ) - if self.__axis == "y" or self.__axis == "cor": - ax.plot( - [sag_0, sag_0 + sag_d, sag_0 + sag_d, sag_0, sag_0], - [ax_0, ax_0, ax_0 + ax_d, ax_0 + ax_d, ax_0], - lw=linewidth, - c=color, - ) - if self.__axis == "x" or self.__axis == "sag": - ax.plot( - [cor_0, cor_0 + cor_d, cor_0 + cor_d, cor_0, cor_0], - [ax_0, ax_0, ax_0 + ax_d, ax_0 + ax_d, ax_0], - lw=linewidth, - c=color, - ) + self.draw_bounding_box_on_axes(axes[0], self.__axis, box) elif len(axes) == 4: ax_ax, _, ax_cor, ax_sag = axes - ax_ax.plot( - [sag_0, sag_0, sag_0 + sag_d, sag_0 + sag_d, sag_0], - [cor_0, cor_0 + cor_d, cor_0 + cor_d, cor_0, cor_0], - lw=linewidth, - c=color, - label=box.name, - ) - ax_cor.plot( - [sag_0, sag_0 + sag_d, sag_0 + sag_d, sag_0, sag_0], - [ax_0, ax_0, ax_0 + ax_d, ax_0 + ax_d, ax_0], - lw=linewidth, - c=color, - ) - ax_sag.plot( - [cor_0, cor_0 + cor_d, cor_0 + cor_d, cor_0, cor_0], - [ax_0, ax_0, ax_0 + ax_d, ax_0 + ax_d, ax_0], - lw=linewidth, - c=color, - ) + self.draw_bounding_box_on_axes(ax_ax, "z", box) + self.draw_bounding_box_on_axes(ax_cor, "y", box, add_label=False) + self.draw_bounding_box_on_axes(ax_sag, "x", box, add_label=False) - def add_legend(self): + def _add_legend(self): """Add a legend to the visualisation""" if len(self.__figure.axes) >= 4: diff --git a/requirements-dev.txt b/requirements-dev.txt index 7ef64e67..dd07cb06 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -10,3 +10,4 @@ sphinx-theme == 1.0 sphinx-click == 2.5.0 pytest == 6.2.2 nbstripout == 0.3.9 +furo == 2021.4.11b34

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