diff --git a/docs/conf.py b/docs/conf.py index b28bab8..d74d07e 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -85,7 +85,10 @@ def setup(app): shutil.copy("../examples/VisualiseData.ipynb", "_examples/VisualiseData.ipynb") shutil.copy("../examples/DoseMetrics.ipynb", "_examples/DoseMetrics.ipynb") shutil.copy("../examples/Radiomics.ipynb", "_examples/Radiomics.ipynb") -shutil.copy( - "../examples/DatasetPreparation.ipynb", "_examples/DatasetPreparation.ipynb" -) +shutil.copy("../examples/DatasetPreparation.ipynb", "_examples/DatasetPreparation.ipynb") shutil.copy("../examples/WorkingWithData.ipynb", "_examples/WorkingWithData.ipynb") +shutil.copy("../examples/WorkingWithStructures.ipynb", "_examples/WorkingWithStructures.ipynb") +shutil.copy("../examples/Configuration.ipynb", "_examples/Configuration.ipynb") +shutil.copy("../examples/ObjectGeneration.ipynb", "_examples/ObjectGeneration.ipynb") +shutil.copy("../examples/AutoSegmentation.ipynb", "_examples/AutoSegmentation.ipynb") +shutil.copy("../examples/nnUNet.ipynb", "_examples/nnUNet.ipynb") diff --git a/docs/dataset.rst b/docs/dataset.rst index cefa3c4..caa441b 100644 --- a/docs/dataset.rst +++ b/docs/dataset.rst @@ -13,3 +13,9 @@ Preparation Functions .. automodule:: pydicer.dataset.functions :members: + +Structure Sets +============== + +.. automodule:: pydicer.dataset.structureset + :members: diff --git a/docs/generate.rst b/docs/generate.rst new file mode 100644 index 0000000..1b4a152 --- /dev/null +++ b/docs/generate.rst @@ -0,0 +1,23 @@ +##################### +Generation +##################### + +Objects +======= + +.. automodule:: pydicer.generate.object + :members: + +Auto-segmentation +================= + +.. automodule:: pydicer.generate.segmentation + :members: + + +Models +====== + +.. automodule:: pydicer.generate.models + :members: + diff --git a/docs/index.rst b/docs/index.rst index 04dbc30..669e340 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -11,6 +11,8 @@ _examples/Radiomics _examples/DoseMetrics _examples/DatasetPreparation + _examples/AutoSegmentation + _examples/nnUNet .. toctree:: :caption: Guides @@ -18,6 +20,9 @@ :hidden: _examples/WorkingWithData + _examples/WorkingWithStructures + _examples/ObjectGeneration + _examples/Configuration .. toctree:: :caption: Developers @@ -35,9 +40,11 @@ tool input config + utils preprocess convert visualise dataset analyse + generate nnunet diff --git a/docs/utils.rst b/docs/utils.rst new file mode 100644 index 0000000..04acec5 --- /dev/null +++ b/docs/utils.rst @@ -0,0 +1,6 @@ +##################### +Utils +##################### + +.. automodule:: pydicer.utils + :members: diff --git a/examples/AutoSegmentation.ipynb b/examples/AutoSegmentation.ipynb new file mode 100644 index 0000000..e501496 --- /dev/null +++ b/examples/AutoSegmentation.ipynb @@ -0,0 +1,453 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Auto-segmentation Inference & Analysis\n", + "\n", + "A common task when working the medical imaging data, it to run an auto-segmentation model\n", + "(inference) on the images in your dataset. If you have manual definitions of the same structures\n", + "available in your dataset, you will typically want to compare the auto and the manual\n", + "segmentations, computing metrics and produce plots and visualisation.\n", + "\n", + "This example notebook will guide you through the process of performing model inference and analysis\n", + "of those structures. We will use a single atlas-based segmentation model for demonstration\n", + "purposes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " from pydicer import PyDicer\n", + "except ImportError:\n", + " !pip install pydicer\n", + " from pydicer import PyDicer\n", + "\n", + "import os\n", + "import logging\n", + "\n", + "from pathlib import Path\n", + "\n", + "import SimpleITK as sitk\n", + "\n", + "from platipy.imaging.registration.utils import apply_transform\n", + "from platipy.imaging.registration.linear import linear_registration\n", + "from platipy.imaging.registration.deformable import fast_symmetric_forces_demons_registration\n", + "\n", + "from pydicer.utils import fetch_converted_test_data\n", + "\n", + "from pydicer.generate.segmentation import segment_image, read_all_segmentation_logs\n", + "\n", + "from pydicer.analyse.compare import (\n", + " compute_contour_similarity_metrics,\n", + " get_all_similarity_metrics_for_dataset,\n", + " prepare_similarity_metric_analysis\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup PyDicer\n", + "\n", + "For this example, we will use the LCTSC test data which has already been converted using PyDicer.\n", + "We also initialise our PyDicer object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "working_directory = fetch_converted_test_data(\"./lctsc_autoseg\", dataset=\"LCTSC\")\n", + "pydicer = PyDicer(working_directory)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare Atlas\n", + "\n", + "Since we will use a single atlas-based segmentation model, we must\n", + "split our data, selecting one case as our `atlas` and the remaining cases as the `validation` set.\n", + "We use the PyDicer `dataset preparation` module to split our dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pydicer.read_converted_data()\n", + "\n", + "# Specify the patient ID to use as the atlas case\n", + "atlas_dataset = \"atlas\"\n", + "atlas_case = \"LCTSC-Train-S1-001\"\n", + "df_atlas = df[df.patient_id==atlas_case]\n", + "\n", + "# And the remaining cases will make up our validation set\n", + "validation_dataset = \"validation\"\n", + "df_validation = df[df.patient_id!=atlas_case]\n", + "\n", + "# Use the dataset preparation module to prepare these two data subsets\n", + "pydicer.dataset.prepare_from_dataframe(atlas_dataset, df_atlas)\n", + "pydicer.dataset.prepare_from_dataframe(validation_dataset, df_validation)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define Segmentation Function\n", + "\n", + "Now that our `atlas` and `validation` sets are ready, we will define our function which will run\n", + "our simple single atlas-based auto-segmentation model for us. This example uses the [atlas-based\n", + "segmentation tools available in PlatiPy](https://pyplati.github.io/platipy/_examples/atlas_segmentation.html#).\n", + "\n", + "In a real-world scenario, you will have your own segmentation model you wish to apply. You should\n", + "integrate this model into such a function which accepts an image and returns a dictionary of\n", + "structures.\n", + "\n", + "To get started, we recommend you try running the [`TotalSegmentator`](https://github.com/wasserth/TotalSegmentator) \n", + "model on your CT data. PyDicer already has a function ready which runs this model, check out\n", + "`run_total_segmentator`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def single_atlas_segmentation(img):\n", + " \"\"\"Segment an image using a single atlas case\n", + "\n", + " Args:\n", + " img (SimpleITK.Image): The SimpleITK image to segment.\n", + "\n", + " Returns:\n", + " dict: The segmented structure dictionary\n", + " \"\"\"\n", + "\n", + " # Load the atlas case image\n", + " atlas_img_row = df_atlas[df_atlas.modality==\"CT\"].iloc[0]\n", + " atlas_img = sitk.ReadImage(str(Path(atlas_img_row.path).joinpath(\"CT.nii.gz\")))\n", + "\n", + " # Load the atlas case structures\n", + " atlas_structures = {}\n", + " atlas_struct_row = df_atlas[df_atlas.modality==\"RTSTRUCT\"].iloc[0]\n", + " for struct_path in Path(atlas_struct_row.path).glob(\"*.nii.gz\"):\n", + " struct_name = struct_path.name.replace(\".nii.gz\", \"\")\n", + " atlas_structures[struct_name] = sitk.ReadImage(str(struct_path))\n", + "\n", + " # Use a simple linear (rigid) registration to align the input image with the atlas image\n", + " img_ct_atlas_reg_linear, tfm_linear = linear_registration(\n", + " fixed_image = img,\n", + " moving_image = atlas_img,\n", + " reg_method='similarity',\n", + " metric='mean_squares',\n", + " optimiser='gradient_descent',\n", + " shrink_factors=[4, 2],\n", + " smooth_sigmas=[2, 0],\n", + " sampling_rate=1.0,\n", + " number_of_iterations=50,\n", + " )\n", + "\n", + " # Perform a fast deformable registration\n", + " img_ct_atlas_reg_dir, tfm_dir, dvf = fast_symmetric_forces_demons_registration(\n", + " img,\n", + " img_ct_atlas_reg_linear,\n", + " ncores=4,\n", + " isotropic_resample=True,\n", + " resolution_staging=[4],\n", + " iteration_staging=[20],\n", + " )\n", + "\n", + " # Combine the two transforms\n", + " tfm_combined = sitk.CompositeTransform((tfm_linear, tfm_dir))\n", + "\n", + " # Apply the transform to the atlas structures\n", + " auto_segmentations = {}\n", + " for s in atlas_structures:\n", + " auto_segmentations[s] = apply_transform(\n", + " atlas_structures[s],\n", + " reference_image=img,\n", + " transform=tfm_combined\n", + " )\n", + "\n", + " return auto_segmentations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run Auto-segmentation\n", + "\n", + "The `segment_dataset` function will run over all images in our dataset and will pass the images to\n", + "a function we define for segmentation. We pass in the name of our `validation_dataset` so that only\n", + "the images in this dataset will be segmented." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "segment_id = \"atlas\" # Used to generate the ID of the resulting auto-segmented structure sets\n", + "\n", + "pydicer.segment_dataset(segment_id, single_atlas_segmentation, dataset_name=validation_dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use PyDicer's `visualisation` module to produce snapshots of the auto-segmentations\n", + "produced." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pydicer.visualise.visualise(force=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read Segmentation Logs\n", + "\n", + "After running the auto-segmentation on across the dataset, we can fetch the logs to confirm that\n", + "everything went well. This will also let us inspect the runtime of the segmentation. In case\n", + "something went wrong, we can use these logs to help debug the issue." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read the segmentation log DataFrame\n", + "df_logs = read_all_segmentation_logs(working_directory)\n", + "df_logs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Use some Pandas magic to produce some stats on the segmentation runtime\n", + "df_success = df_logs[df_logs.success_flag]\n", + "agg_stats = [\"mean\", \"std\", \"max\", \"min\", \"count\"]\n", + "df_success[[\"segment_id\", \"total_time_seconds\"]].groupby(\"segment_id\").agg(agg_stats)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Auto-segmentation Analysis\n", + "\n", + "Now that our auto-segmentation model has been run on our `validation` set, we can compare these\n", + "structures to the manual structures available on this dataset. PyDicer provides functionality to\n", + "compute similarity metrics, but we must first prepare a DataFrame containing our auto structure\n", + "sets (`df_target`) and a separate DataFrame with our manual structure sets (`df_reference`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pydicer.read_converted_data(dataset_name=validation_dataset)\n", + "df_structs = df[df.modality==\"RTSTRUCT\"]\n", + "\n", + "df_reference = df_structs[~df_structs.hashed_uid.str.startswith(f\"atlas_\")]\n", + "df_target = df_structs[df_structs.hashed_uid.str.startswith(f\"atlas_\")]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_reference" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_target" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute Similarity \n", + "\n", + "We use the `compute_contour_similarity_metrics` function to compute the metrics comparing our\n", + "target structures to our reference structures.\n", + "\n", + "We can specify which metrics we want to compute, in this example we compute the Dice Similarity\n", + "Coefficient (DSC), Hausdorff Distance, Mean Surface Distance and the Surface DSC.\n", + "\n", + "> Structure names must match exactly, so we use a structure name mapping to standardise our\n", + "> structure names prior to computing the similarity metrics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Add our structure name mapping\n", + "mapping_id = \"nnunet_lctsc\"\n", + "mapping = {\n", + " \"Esophagus\": [],\n", + " \"Heart\": [],\n", + " \"Lung_L\": [\"L_Lung\", \"Lung_Left\"],\n", + " \"Lung_R\": [\"Lung_Right\"],\n", + " \"SpinalCord\": [\"SC\"],\n", + "}\n", + "pydicer.add_structure_name_mapping(mapping, mapping_id)\n", + "\n", + "# Specify the metrics we want to compute\n", + "compute_metrics = [\"DSC\", \"hausdorffDistance\", \"meanSurfaceDistance\", \"surfaceDSC\"]\n", + "\n", + "# Compute the similarity metrics\n", + "compute_contour_similarity_metrics(\n", + " df_target,\n", + " df_reference,\n", + " segment_id,\n", + " compute_metrics=compute_metrics,\n", + " mapping_id=mapping_id\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fetch the similarity metrics\n", + "\n", + "Here we fetch the metrics computed and output some stats. Note that if a segmentation fails, \n", + "surface metrics will return NaN and will be excluded from these stats." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fetch the similarity metrics\n", + "df_metrics = get_all_similarity_metrics_for_dataset(\n", + " working_directory,\n", + " dataset_name=validation_dataset,\n", + " structure_mapping_id=mapping_id\n", + ")\n", + "\n", + "# Aggregate the stats using Pandas\n", + "df_metrics[\n", + " [\"segment_id\", \"structure\", \"metric\", \"value\"]\n", + " ].groupby(\n", + " [\"segment_id\", \"structure\", \"metric\"]\n", + " ).agg(\n", + " [\"mean\", \"std\", \"min\", \"max\", \"count\"]\n", + " )\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Perform Analysis\n", + "\n", + "There are various plots and visualisations you may wish to produce following computation of\n", + "similarity metrics. The `prepare_similarity_metric_analysis` will generate several useful plots\n", + "which will serve as a useful starting point when analysing your auto-segmentation results.\n", + "\n", + "Plots are generated in a directory you provide. In this example, plots and tables (`.csv`) are\n", + "output in the `testdata_lctsc/analysis/atlas` directory. Run the following cell, then navigate\n", + "to that directory to explore the results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "analysis_output_directory = working_directory.joinpath(\n", + " \"analysis\",\n", + " segment_id\n", + ")\n", + "analysis_output_directory.mkdir(parents=True, exist_ok=True)\n", + "\n", + "prepare_similarity_metric_analysis(\n", + " working_directory,\n", + " analysis_output_directory=analysis_output_directory,\n", + " dataset_name=validation_dataset,\n", + " structure_mapping_id=mapping_id\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "814af119db7f8f2860617be3dcd1d37c560587d11c65bd58c45b1679d3ee6ea4" + }, + "kernelspec": { + "display_name": "Python 3.8.0 64-bit ('pydicer': pyenv)", + "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": "3.8.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/Configuration.ipynb b/examples/Configuration.ipynb new file mode 100644 index 0000000..0559bd6 --- /dev/null +++ b/examples/Configuration.ipynb @@ -0,0 +1,243 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Configuration\n", + "\n", + "PyDicer provides various options which you may configure to change the behaviour of the tool." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " from pydicer import PyDicer\n", + "except ImportError:\n", + " !pip install pydicer\n", + " from pydicer import PyDicer\n", + "\n", + "import logging\n", + "\n", + "from pydicer.utils import fetch_converted_test_data\n", + "\n", + "working_directory = fetch_converted_test_data(\"./testdata_lctsc\", dataset=\"LCTSC\")\n", + "\n", + "pydicer = PyDicer(working_directory)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Getting and Setting Options\n", + "\n", + "Use the `get_config` and `set_config` functions of the `config` module to get and set configuration\n", + "options respectively." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "logging_verbosity = pydicer.config.get_config(\"verbosity\")\n", + "print(f\"Current logging verbosity: {logging_verbosity}\")\n", + "\n", + "# Set to logging level DEBUG\n", + "pydicer.config.set_config(\"verbosity\", logging.DEBUG)\n", + "\n", + "logging_verbosity = pydicer.config.get_config(\"verbosity\")\n", + "print(f\"New logging verbosity: {logging_verbosity}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Options Available\n", + "\n", + "### Logging Verbosity\n", + "\n", + "Level of output for standard out. Value indicates the Python built-in log level. A value of 0\n", + "(not set) will display the process bar. Logs of all levels are available in the .pydicer directory.\n", + "\n", + "Valid options are: `[logging.NOTSET, logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR]`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pydicer.config.set_config(\"verbosity\", logging.DEBUG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Frame of Reference Fallback Linkage\n", + "\n", + "Determine whether to fallback on linking objects via their Frame of Reference if no more stable\n", + "link exists.\n", + "\n", + "Valid options are: `True` or `False`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pydicer.config.set_config(\"for_fallback_linkage\", True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Enforce `.dcm` file extension\n", + "\n", + "If True only files with the .dcm or .DCM extension will be preprocessed. Otherwise any file in the\n", + "DICOM directory will be preprocessed.\n", + "\n", + "Valid options are: `True` or `False`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pydicer.config.set_config(\"enforce_dcm_ext\", True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interpolate Missing Slices\n", + "\n", + "When missing slices are detected these will be interpolated if True. Otherwise these cases will be\n", + "sent to quarantine.\n", + "\n", + "Valid options are: `True` or `False`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pydicer.config.set_config(\"interp_missing_slices\", True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ignore Duplicate Slices\n", + "\n", + "If two slices at the same location with different pixel data are found then the first slice is used\n", + "if ignore_duplicate_slices is True. Otherwise an error is raised and these images are sent to\n", + "quarantine.\n", + "\n", + "Valid options are: `True` or `False`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pydicer.config.set_config(\"ignore_duplicate_slices\", False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate NRRD\n", + "\n", + "Whether or not to generate an additional NRRD file when converting RTSTRUCT. This allows loading\n", + "easily into 3D slicer, but it takes up more disk space and takes time to generate the file.\n", + "\n", + "Valid options are: `True` or `False`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pydicer.config.set_config(\"generate_nrrd\", False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### NRRD Colormap\n", + "\n", + "If NRRD files are to be generated, this defines the Matplotlib colormap to use when saving NRRD\n", + "file of structures.\n", + "\n", + "Valid options are any [Matplotlib colormap](https://matplotlib.org/stable/users/explain/colors/colormaps.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pydicer.config.set_config(\"nrrd_colormap\", \"rainbow\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "814af119db7f8f2860617be3dcd1d37c560587d11c65bd58c45b1679d3ee6ea4" + }, + "kernelspec": { + "display_name": "Python 3.8.0 64-bit ('pydicer': pyenv)", + "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": "3.9.16" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/ObjectGeneration.ipynb b/examples/ObjectGeneration.ipynb new file mode 100644 index 0000000..da5c449 --- /dev/null +++ b/examples/ObjectGeneration.ipynb @@ -0,0 +1,325 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Generating Data Objects\n", + "\n", + "While PyDicer primarily deals with converting data objects from DICOM, there are instances where\n", + "you may want to generate a new data object and have it integrated into your PyDicer dataset.\n", + "\n", + "Some examples of when you may want to do this are:\n", + "- Generate a new dose grid with EQD2 correction applied.\n", + "- Generate a structure set of auto-segmented structures.\n", + "- Generate a Pseudo-CT image from an MRI.\n", + "\n", + "In this guide we show you how to generate new data objects to help you perform tasks such as those\n", + "described in the examples mentioned above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " from pydicer import PyDicer\n", + "except ImportError:\n", + " !pip install pydicer\n", + " from pydicer import PyDicer\n", + "\n", + "from pathlib import Path\n", + "import SimpleITK as sitk\n", + "\n", + "from pydicer.utils import fetch_converted_test_data, load_object_metadata, read_simple_itk_image\n", + "\n", + "working_directory = fetch_converted_test_data(\"./testdata_hnscc\", dataset=\"HNSCC\")\n", + "\n", + "pydicer = PyDicer(working_directory)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Dose Objects\n", + "\n", + "In the following cell, we:\n", + "- Iterate over each dose grid in our dataset\n", + "- Load the dose grid using SimpleITK\n", + "- Apply EQD2 dose correction (hard coded for demonstration purposes)\n", + "- Save the corrected dose as a new object in our dataset\n", + "\n", + "Once the dose object is saved, when you compute DVHs and dose metrics, this new dose will appear\n", + "in that data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "alpha_beta = 2\n", + "\n", + "df = pydicer.read_converted_data()\n", + "df_doses = df[df[\"modality\"] == \"RTDOSE\"]\n", + "\n", + "for _, dose_row in df_doses.iterrows():\n", + " \n", + " if \"EQD2_ab\" in dose_row.hashed_uid:\n", + " # This is an already scaled dose\n", + " continue\n", + " \n", + " df_linked_plan = df[\n", + " df[\"sop_instance_uid\"] == dose_row.referenced_sop_instance_uid\n", + " ]\n", + "\n", + " linked_plan = df_linked_plan.iloc[0]\n", + " ds_plan = load_object_metadata(linked_plan)\n", + "\n", + " # Read the planned fractions from the plan object\n", + " fractions = int(ds_plan.FractionGroupSequence[0].NumberOfFractionsPlanned)\n", + "\n", + " print(f\"{dose_row.patient_id} has {fractions} fractions\")\n", + " \n", + " # Load the dose grid\n", + " dose_path = Path(dose_row.path).joinpath(\"RTDOSE.nii.gz\")\n", + " dose = sitk.ReadImage(str(dose_path))\n", + " dose = sitk.Cast(dose, sitk.sitkFloat64)\n", + "\n", + " dose_id = f\"{dose_row.hashed_uid}_EQD2_ab{alpha_beta}\"\n", + " \n", + " if len(df_doses[df_doses.hashed_uid == dose_id]) > 0:\n", + " print(f\"Already converted dose for {dose_id}\")\n", + " continue\n", + " \n", + " # Apply the EQD2 correction\n", + " eqd2_dose = dose * (((dose/actual_frac) + alpha_beta) / (2 + alpha_beta))\n", + " \n", + " # Save off the new dose grid\n", + " try:\n", + " print(f\"Saving dose grid with ID: {dose_id}\")\n", + " pydicer.add_dose_object(\n", + " eqd2_dose,\n", + " dose_id,\n", + " dose_row.patient_id,\n", + " linked_plan,\n", + " dose_row.for_uid\n", + " )\n", + " except SystemError:\n", + " print(f\"Dose object {dose_id} already exists!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can load our data objects, and check that our new dose grids are stored alongside our\n", + "converted data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pydicer.read_converted_data()\n", + "df[df.modality==\"RTDOSE\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Structure Set Objects\n", + "\n", + "In this example, we:\n", + "- Iterate over each CT image in our dataset\n", + "- Load the CT image using SimpleITK, and apply a threshold to segment bones\n", + "- Save the segmented bones as a new structure set object\n", + "\n", + "> Note: This specific functionality is supported by the auto-segmentation inference module. If you\n", + "> are using this to generate auto-segmentations it is recommended you use that functionality." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bone_threshold = 300 # Set threshold at 300 HU\n", + "\n", + "df = pydicer.read_converted_data()\n", + "df_cts = df[df[\"modality\"] == \"CT\"]\n", + "\n", + "for idx, ct_row in df_cts.iterrows():\n", + "\n", + " # Load the image\n", + " img = read_simple_itk_image(ct_row)\n", + "\n", + " # Apply the threshold\n", + " bone_mask = img > bone_threshold\n", + "\n", + " # Save the mask in a new structure set\n", + " structure_set_id = f\"bones_{ct_row.hashed_uid}\"\n", + " new_structure_set = {\n", + " \"bones\": bone_mask\n", + " }\n", + "\n", + "\n", + " try:\n", + " print(f\"Saving structure set with ID: {structure_set_id}\")\n", + " pydicer.add_structure_object(\n", + " new_structure_set,\n", + " structure_set_id,\n", + " ct_row.patient_id,\n", + " ct_row,\n", + " )\n", + " except SystemError:\n", + " print(f\"Structure Set {structure_set_id} already exists!\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's load out data objects to see if we have our new structure sets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pydicer.read_converted_data()\n", + "df[df.modality==\"RTSTRUCT\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can also run the visualise module. Use the `force=False` flag to ensure that only the newly\n", + "generated objects are visualised" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pydicer.visualise.visualise(force=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Take a look inside the `testdata_hnscc/data` directory for the new structure set folders. See the\n", + "visualised snapshot to check that our bone segmentation worked!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Image Objects\n", + "\n", + "In this example, we:\n", + "- Iterate over each CT image in our dataset\n", + "- Load the CT image using SimpleITK, and apply a Laplacian Sharpening image filter to it\n", + "- Save the image as a new image object" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pydicer.read_converted_data()\n", + "df_cts = df[df[\"modality\"] == \"CT\"]\n", + "\n", + "for idx, ct_row in df_cts.iterrows():\n", + "\n", + " # Load the image\n", + " img = read_simple_itk_image(ct_row)\n", + "\n", + " # Sharpen the image\n", + " img_sharp = sitk.LaplacianSharpening(img)\n", + "\n", + " # Save the sharpened image\n", + " img_id = f\"sharp_{ct_row.hashed_uid}\"\n", + " \n", + " try:\n", + " print(f\"Saving image with ID: {img_id}\")\n", + " pydicer.add_image_object(\n", + " img_sharp,\n", + " img_id,\n", + " ct_row.modality,\n", + " ct_row.patient_id,\n", + " for_uid=ct_row.for_uid\n", + " )\n", + " except SystemError:\n", + " print(f\"Image {img_id} already exists!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can visualise the images and produce snapshots once more. Find the sharpen images in the\n", + "working directory. Can you see the difference between the sharpened CT and the original?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pydicer.visualise.visualise(force=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "814af119db7f8f2860617be3dcd1d37c560587d11c65bd58c45b1679d3ee6ea4" + }, + "kernelspec": { + "display_name": "Python 3.8.0 64-bit ('pydicer': pyenv)", + "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": "3.9.16" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/WorkingWithData.ipynb b/examples/WorkingWithData.ipynb index 6142459..aad36f3 100644 --- a/examples/WorkingWithData.ipynb +++ b/examples/WorkingWithData.ipynb @@ -27,11 +27,12 @@ "\n", "from pathlib import Path\n", "\n", - "import SimpleITK as sitk\n", - "\n", - "from pydicer.utils import fetch_converted_test_data\n", - "\n", - "from pydicer.utils import load_object_metadata, determine_dcm_datetime" + "from pydicer.utils import (\n", + " fetch_converted_test_data,\n", + " load_object_metadata,\n", + " determine_dcm_datetime,\n", + " read_simple_itk_image\n", + ")" ] }, { @@ -95,8 +96,7 @@ "\n", " print(f\"Loading image with hashed UID: {ct_row.hashed_uid}...\", end=\"\")\n", "\n", - " img_path = Path(ct_row.path).joinpath(\"CT.nii.gz\")\n", - " img = sitk.ReadImage(str(img_path))\n", + " img = read_simple_itk_image(ct_row)\n", "\n", " print(\" Complete\")" ] diff --git a/examples/WorkingWithStructures.ipynb b/examples/WorkingWithStructures.ipynb new file mode 100644 index 0000000..70ee45f --- /dev/null +++ b/examples/WorkingWithStructures.ipynb @@ -0,0 +1,326 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Working with Structures\n", + "\n", + "In PyDicer, structure sets which have been converted from the DICOM `RTSTRUCT` modality are stored\n", + "within the directory structure. One common issue when working with real world datasets is that\n", + "structure names are often inconsistent requiring standardisation of names prior to analysing data.\n", + "\n", + "In PyDicer, structure name standardisation is achieved by defining structure mapping dictionaries\n", + "which can be stored globally (applied to all structure sets) or locally (specific mapping per\n", + "structure set or per patient).\n", + "\n", + "In this guide we present some examples on how to define such structure name mappings and will\n", + "introduce the `StructureSet` class which simplifies loading and working with structure objects." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " from pydicer import PyDicer\n", + "except ImportError:\n", + " !pip install pydicer\n", + " from pydicer import PyDicer\n", + "\n", + "from pathlib import Path\n", + "\n", + "import SimpleITK as sitk\n", + "\n", + "from pydicer.utils import fetch_converted_test_data, add_structure_name_mapping\n", + "from pydicer.dataset.structureset import StructureSet" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup PyDicer\n", + "\n", + "Here we load the LCTSC data which has already been converted. This is downloaded into the\n", + "`testdata_lctsc` directory. We also initialise a `PyDicer` object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "working_directory = fetch_converted_test_data(\"./testdata_lctsc\", dataset=\"LCTSC\")\n", + "\n", + "pydicer = PyDicer(working_directory)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Structures with `StructureSet`\n", + "\n", + "With the `StructureSet`, we can load structures in a structure set, with the structure name being\n", + "the key and the `SimpleITK` Image of the mask as the value.\n", + "\n", + "In the following cell, we create a `StructureSet` object, determine the names of the structures in\n", + "that structure set, and iterate over each structure, printing the sum of all voxel values in the\n", + "mask (for demonstration purposes)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the converted data\n", + "df = pydicer.read_converted_data()\n", + "df_structs = df[df.modality==\"RTSTRUCT\"]\n", + "\n", + "# Create a StructureSet for the first row\n", + "struct_row = df_structs.iloc[0]\n", + "structure_set = StructureSet(struct_row)\n", + "\n", + "structure_names = structure_set.structure_names\n", + "print(f\"Structure names: {structure_names}\")\n", + "\n", + "for structure in structure_names:\n", + " mask = sitk.GetArrayFromImage(structure_set[structure])\n", + " print(f\"Mask voxel sum for {structure}: {mask.sum()}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the next cell, we iterate over all our structure sets, and print out the names of the structures\n", + "available. Notice that for some structure sets, structures aren't named consistently. In the next\n", + "section we will resolve this with a structure name mapping." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pydicer.read_converted_data()\n", + "df_structs = df[df.modality==\"RTSTRUCT\"]\n", + "\n", + "for idx, struct_row in df_structs.iterrows():\n", + " structure_set = StructureSet(struct_row)\n", + "\n", + " structure_names = structure_set.structure_names\n", + " print(f\"Patient: {struct_row.patient_id}, Structure names: {structure_names}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add Structure Name Mapping\n", + "\n", + "Structure name mappings are defined as Python dictionaries, with the standardised structure name\n", + "as the key, and the value a list of name variations which should map to the standardised name.\n", + "\n", + "Use the `add_structure_name_mapping` to add a mapping. A `mapping_id` may be supplied to refer\n", + "to different mappings. If no `mapping_id` is supplied, a default mapping id is used.\n", + "\n", + "If a `structure_set_row` or `patient_id` is supplied, then the mapping will be stored at the\n", + "corresponding level. If neither is supplied, the mapping will be stored globally for all structure\n", + "sets in the datasets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mapping = {\n", + " \"Esophagus\": [],\n", + " \"Heart\": [],\n", + " \"Lung_L\": [\"Lung_Left\"],\n", + " \"Lung_R\": [\"Lung_Right\"],\n", + " \"SpinalCord\": [\"SC\"],\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pydicer.add_structure_name_mapping(mapping)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The default mapping has been saved. You can find the saved mapping in the\n", + "`testdata_lctsc/.pydicer/.structure_set_mappings` directory." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can check our `StructureSet` to confirm the names are mapped properly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for idx, struct_row in df_structs.iterrows():\n", + " structure_set = StructureSet(struct_row)\n", + "\n", + " structure_names = structure_set.structure_names\n", + " print(f\"Patient: {struct_row.patient_id}, Structure names: {structure_names}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Subsets of Structures\n", + "\n", + "Structure name mappings are also useful if you only want to work with a subset of structures\n", + "available. Simply leave them out of the mapping entirely, and they won't be loaded as part of the\n", + "`StructureSet`.\n", + "\n", + "In this example, we use a `mapping_id` of `struct_subset` to keep this mapping separate from the\n", + "mapping defined above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mapping_id = \"struct_subset\"\n", + "sub_mapping = {\n", + " \"Lung_L\": [\"Lung_Left\"],\n", + " \"Lung_R\": [\"Lung_Right\"],\n", + "}\n", + "\n", + "pydicer.add_structure_name_mapping(sub_mapping, mapping_id=mapping_id)\n", + "\n", + "for idx, struct_row in df_structs.iterrows():\n", + " structure_set = StructureSet(struct_row, mapping_id=mapping_id) # Provide the mapping_id!\n", + "\n", + " structure_names = structure_set.structure_names\n", + " print(f\"Patient: {struct_row.patient_id}, Structure names: {structure_names}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Local Structure Name Mappings\n", + "\n", + "Next, we only specify a mapping for one specific structure set. We will use `local_mapping` as the\n", + "`mapping_id`. In the output you will see that only one structure set has had the mapping applied." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mapping_id = \"local_mapping\"\n", + "mapping = {\n", + " \"Esophagus\": [],\n", + " \"Heart\": [],\n", + " \"Lung_L\": [\"Lung_Left\"],\n", + " \"Lung_R\": [\"Lung_Right\"],\n", + " \"SpinalCord\": [\"SC\"],\n", + "}\n", + "\n", + "struct_row = df[(df.patient_id==\"LCTSC-Train-S1-002\") & (df.modality==\"RTSTRUCT\")].iloc[0]\n", + "\n", + "# Only adding mapping for one structure set\n", + "pydicer.add_structure_name_mapping(\n", + " mapping,\n", + " mapping_id=mapping_id,\n", + " structure_set_row=struct_row\n", + ")\n", + "\n", + "for idx, struct_row in df_structs.iterrows():\n", + " structure_set = StructureSet(struct_row, mapping_id=mapping_id) # Provide the mapping_id!\n", + "\n", + " structure_names = structure_set.structure_names\n", + " print(f\"Patient: {struct_row.patient_id}, Structure names: {structure_names}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that mapping has only been applied to the structure set for patient `LCTSC-Train-S1-002`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using Mappings in PyDicer\n", + "\n", + "Once mappings are defined, these can be used when you:\n", + "- Compute Dose Metrics\n", + "- Fetch Radiomics Features\n", + "- Analyse Auto-segmentations\n", + "- Prepare data for nnUNet training\n", + "\n", + "Check out the documentation for those modules to see where you can supply your `mapping_id` to have\n", + "the structure set standardisation applied. If you have used the default `mapping_id`, the\n", + "standardisation will be applied automatically." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "814af119db7f8f2860617be3dcd1d37c560587d11c65bd58c45b1679d3ee6ea4" + }, + "kernelspec": { + "display_name": "Python 3.8.0 64-bit ('pydicer': pyenv)", + "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": "3.9.16" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/nnUNet.ipynb b/examples/nnUNet.ipynb new file mode 100644 index 0000000..f966785 --- /dev/null +++ b/examples/nnUNet.ipynb @@ -0,0 +1,365 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# nnUNet Data Preparation\n", + "\n", + "The [nnUNet](https://github.com/MIC-DKFZ/nnUNet) is a self-configuring method for deep\n", + "learning-based biomedical image segmentation. However it does require data to be formatted in a\n", + "specific way on the file system. In this notebook, we demonstrate some useful functionality to\n", + "prepare a dataset converted by PyDicer for training using nnUNet.\n", + "\n", + "> Note: PyDicer currently only supports nnUNet v1. Contributions adding support for nnUNet v2 are\n", + "> welcome." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " from pydicer import PyDicer\n", + "except ImportError:\n", + " !pip install pydicer\n", + " from pydicer import PyDicer\n", + "\n", + "import os\n", + "import logging\n", + "\n", + "from pathlib import Path\n", + "\n", + "from pydicer.utils import fetch_converted_test_data\n", + "\n", + "from pydicer.dataset.nnunet import NNUNetDataset\n", + "from pydicer.dataset.structureset import StructureSet\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup nnUNet\n", + "\n", + "Consult the [nnUNet documentation](https://github.com/MIC-DKFZ/nnUNet/tree/master/documentation) \n", + "for details on how to install nnUNet, setup folder paths and conduct model training. The dataset\n", + "will be prepared in the `nnUNet_raw_data_base` directory. If you already have this set in your\n", + "environment you can remove the following cell. For demonstration purposes, we set our \n", + "`nnUNet_raw_data_base` to a scratch directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"nnUNet_raw_data_base\"] = \"./nnScratch\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup PyDicer\n", + "\n", + "For this example, we will use the LCTSC test data which has already been converted using PyDicer.\n", + "We also initialise our PyDicer object.\n", + "\n", + "For working with nnUNet, we set the PyDicer logging verbosity to `INFO`, so that we can see the\n", + "relevant output being generated by the tool." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "working_directory = fetch_converted_test_data(\"./testdata_lctsc\", dataset=\"LCTSC\")\n", + "pydicer = PyDicer(working_directory)\n", + "pydicer.set_verbosity(logging.INFO)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define Structures\n", + "\n", + "PyDicer uses the [structure name mapping](https://australiancancerdatanetwork.github.io/pydicer/_examples/WorkingWithStructures.html) functionality to determine which structures to train\n", + "the nnUNet model for. Here we add a structure name mapping for this task." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mapping_id = \"nnunet_lctsc\"\n", + "mapping = {\n", + " \"Esophagus\": [],\n", + " \"Heart\": [],\n", + " \"Lung_L\": [\"L_Lung\", \"Lung_Left\"],\n", + " \"Lung_R\": [\"Lung_Right\"],\n", + " \"SpinalCord\": [\"SC\"],\n", + "}\n", + "\n", + "pydicer.add_structure_name_mapping(\n", + " mapping_id=mapping_id,\n", + " mapping_dict=mapping\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialise `NNUNetDataset` object\n", + "\n", + "The `NNUNetDataset` class provides the functionality to prepare a dataset from PyDicer data. Here\n", + "we create an object of this class for use in this example. Check out the \n", + "[documentation](https://australiancancerdatanetwork.github.io/pydicer/nnunet.html) for more\n", + "information on how the `NNUNetDataset` class works." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nnunet_task_id = 123\n", + "nnunet_task_name = \"LCTSC_Test\"\n", + "nnunet_task_description = \"A dummy nnUNet task for demonstration purposes\"\n", + "\n", + "nnunet = NNUNetDataset(\n", + " working_directory,\n", + " nnunet_task_id,\n", + " nnunet_task_name,\n", + " nnunet_task_description,\n", + " mapping_id=mapping_id\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inspect Dataset\n", + "\n", + "Our `NNUNetDataset` tool expects to have exactly one image and one structure set per patient\n", + "(multi-modal training not yet supported, contributions welcome). Let's fetch our converted\n", + "DataFrame to confirm that this is the case.\n", + "\n", + "If your dataset isn't yet in such as state, you can use the \n", + "[`dataset preparation`](https://australiancancerdatanetwork.github.io/pydicer/_examples/DatasetPreparation.html)\n", + "module in PyDicer to prepare a subset of data. Once the dataset is prepared, pass the\n", + "`dataset_name` argument when creating the `NNUNetDataset` object above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pydicer.read_converted_data(working_directory)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check Dataset\n", + "\n", + "The `check_dataset` function confirms that we have one image and one structure set per patient in \n", + "our dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nnunet.check_dataset()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Split Dataset\n", + "\n", + "Here we randomly split our dataset into a training and testing set. You can specify the\n", + "`training_cases` and `testing_cases` to use in the `split_dataset` function. If these aren't\n", + "supplied, the [`train_test_split`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)\n", + "function from `sklearn` will be used. You can pass keyword arguments to this function via the\n", + "`split_dataset` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nnunet.split_dataset()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check for Duplicate Data\n", + "\n", + "Now that the dataset is split, we must ensure that none of the `training_cases` are present in the\n", + "`testing_cases`. Even if the cases have different IDs, it is possible that through anonymisation\n", + "the same patient is anonymised to two different IDs. The `check_duplicates_train_test` function\n", + "will check the imaging data to ensure there are no duplicates." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nnunet.check_duplicates_train_test()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check Structure Names\n", + "\n", + "The nnUNet requires that all structures are present for all cases (missing structures are not\n", + "supported). The `check_structure_names` function will output a grid indicating where structures\n", + "might be missing (or a structure name mapping is missing).\n", + "\n", + "If there are any cases for which any of the structures are missing, this should be resolved (by\n", + "adding a structure mapping or remove the case from the dataset) before proceeding." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = nnunet.check_structure_names()\n", + "df_results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check for Overlapping Structures\n", + "\n", + "nnUNet (v1) is unable to handle structures which are overlapping. If there are structures which are\n", + "overlapping, the PyDicer tool will assign the overlapping voxels to the smaller structure (to \n", + "assign to the larger structure, set `nnunet.assign_overlap_to_largest=False`).\n", + "\n", + "The `check_overlapping_structures` function will log any structures which are overlapping and will\n", + "be affected by this rule." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nnunet.check_overlapping_structures()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare nnUNet Dataset\n", + "\n", + "Now that all checks are complete, we can proceed with preparing the nnUNet dataset. Take a look in\n", + "the dataset directory after the cell finishes running to confirm that everything worked as\n", + "expected." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nnunet_dataset_path = nnunet.prepare_dataset()\n", + "print(f\"Dataset prepared in: {nnunet_dataset_path}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare nnUNet Training Scripts\n", + "\n", + "Consult the [nnUNet documentation](https://github.com/MIC-DKFZ/nnUNet/tree/master/documentation)\n", + "for information on model training. The `generate_training_scripts` may help prepare a script useful\n", + "for training the nnUNet models for the dataset which was prepared." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Add some additional commands at the top of the script (useful for activating a virtual\n", + "# environemnt)\n", + "script_header = [\n", + " '# source /path/to/venv/bin/activate',\n", + "]\n", + "\n", + "script_path = nnunet.generate_training_scripts(script_header=script_header)\n", + "print(f\"Training script ready in: {script_path}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "814af119db7f8f2860617be3dcd1d37c560587d11c65bd58c45b1679d3ee6ea4" + }, + "kernelspec": { + "display_name": "Python 3.8.0 64-bit ('pydicer': pyenv)", + "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": "3.8.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/poetry.lock b/poetry.lock index edaa397..e5f9052 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. 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join_working_directory=False ) if len(df_converted[df_converted.hashed_uid == object_id]) > 0: - raise SystemError(f"An object with ID {object_id} already exists for this patient!") + raise SystemError( + f"An object with ID {object_id} already exists for this patient!" + ) # Do a few checks about the files within the data object directory. Won't raise any errors here # but will log warnings if something doesn't seem right @@ -125,7 +129,6 @@ def add_object( datasets = [datasets] for dataset in datasets: - # Make sure the dataset directory exists dataset_directory = working_directory.joinpath(dataset) if not dataset_directory.exists(): @@ -135,7 +138,9 @@ def add_object( # Make sure the patient directory exists patient_directory = dataset_directory.joinpath(patient_id) if not patient_directory.exists(): - logger.warning("Patient %s doesn't exist in dataset %s", patient_id, dataset) + logger.warning( + "Patient %s doesn't exist in dataset %s", patient_id, dataset + ) continue df_converted = read_converted_data( @@ -150,18 +155,6 @@ def add_object( df_converted.to_csv(patient_directory.joinpath("converted.csv")) -# Add image -# def add_image_object( -# working_directory, image, image_id, patient_id, modality, for_uid=None, datasets=None -# ): - -# create the folder - -# save the image - -# add the object to pydicer - - def get_linked_for_and_ref_uid(working_directory, patient_id, linked_obj): """Determine the linked frame of reference UID and SOP instance UID @@ -181,16 +174,16 @@ def get_linked_for_and_ref_uid(working_directory, patient_id, linked_obj): for_uid = None ref_sop_uid = None if linked_obj is not None: - if isinstance(linked_obj, str): - df_converted = read_converted_data( working_directory, patients=[patient_id], join_working_directory=False ) df_linked = df_converted[df_converted.hashed_uid == linked_obj] if not len(df_linked) == 1: - raise SystemError(f"Expecting one linked_plan object but found {len(df_linked) }") + raise SystemError( + f"Expecting one linked_plan object but found {len(df_linked) }" + ) linked_obj = df_linked.iloc[0] @@ -200,6 +193,71 @@ def get_linked_for_and_ref_uid(working_directory, patient_id, linked_obj): return for_uid, ref_sop_uid +def add_image_object( + working_directory, + image, + image_id, + modality, + patient_id, + linked_image=None, + for_uid=None, + datasets=None, +): + """Add a generated image object to the project. + + Args: + working_directory (pathlib.Path): The project working directory. + image (sitk.Image): The SimpleITK.Image object to save. + image_id (str): The unique ID of the new image object. + modality (str): The modality of the image being saved. + patient_id (str): The ID of the patient of this object. + linked_image (str|pd.Series, optional): The hashed_uid or the Pandas DataFrame row of the + image object to link to. If None the new object won't be linked. Defaults to None. + for_uid (str, optional): Frame Of Reference UID for new data object. If not provided it + will be extracted from the linked image (if available). Defaults to None. + datasets (list|str, optional): The name(s) of the dataset(s) to add the object to. Defaults + to None. + + Raises: + ValueError: Raised then the patient ID does not exist + SystemError: Raised when a linked_image is provided but can't be found for this patient. + """ + + # Check that the patient directory exists + patient_directory = working_directory.joinpath(CONVERTED_DIR_NAME, patient_id) + if not patient_directory.exists(): + raise ValueError(f"Patient with ID {patient_id} does not exist.") + + object_type = "image" + + # Grab the UIDs of the frame of reference and the linked SOP instance + linked_for_uid, ref_sop_uid = get_linked_for_and_ref_uid( + working_directory, patient_id, linked_image + ) + + if for_uid is None: + for_uid = linked_for_uid + + image_object_path = patient_directory.joinpath(f"{object_type}s", image_id) + image_object_path.mkdir(exist_ok=True, parents=True) + + # Save the data object in the image_object_path + image_file = image_object_path.joinpath(f"{modality}.nii.gz") + sitk.WriteImage(image, str(image_file)) + + # Add the object to pydicer + add_object( + working_directory, + image_id, + patient_id, + object_type, + modality, + for_uid=for_uid, + referenced_sop_instance_uid=ref_sop_uid, + datasets=datasets, + ) + + def add_structure_object( working_directory, structures, @@ -247,9 +305,11 @@ def add_structure_object( structure_object_path = patient_directory.joinpath(f"{object_type}s", structure_id) structure_object_path.mkdir(exist_ok=True, parents=True) - # Save the data object in the dose_object_path + # Save the data object in the structure_object_path for struct_name, struct_mask in structures.items(): - sitk.WriteImage(struct_mask, str(structure_object_path.joinpath(f"{struct_name}.nii.gz"))) + sitk.WriteImage( + struct_mask, str(structure_object_path.joinpath(f"{struct_name}.nii.gz")) + ) # Add the object to pydicer add_object( @@ -265,7 +325,13 @@ def add_structure_object( def add_dose_object( - working_directory, dose, dose_id, patient_id, linked_plan=None, for_uid=None, datasets=None + working_directory, + dose, + dose_id, + patient_id, + linked_plan=None, + for_uid=None, + datasets=None, ): """Add a generated dose object to the project. diff --git a/pydicer/generate/segmentation.py b/pydicer/generate/segmentation.py index f504fd7..49f8cb7 100644 --- a/pydicer/generate/segmentation.py +++ b/pydicer/generate/segmentation.py @@ -113,12 +113,16 @@ def write_autoseg_log( def read_all_segmentation_logs( - working_directory: Path, dataset_name: str, segment_id: str = None, modality: str = None + working_directory: Path, + dataset_name: str = CONVERTED_DIR_NAME, + segment_id: str = None, + modality: str = None, ): """Read all auto-segmentation logs in a dataset. Args: dataset_name (str): The name of the dataset to read for. segment_id (str): The ID of the auto-segmentation run. + modality (str): The modality of the images to read logs for. Returns: pd.DataFrame: The pandas DataFrame object with all logs for the dataset. diff --git a/pydicer/tool.py b/pydicer/tool.py index 07d47f7..e2a0e37 100644 --- a/pydicer/tool.py +++ b/pydicer/tool.py @@ -24,7 +24,12 @@ ) from pydicer.quarantine import read_quarantined_data -from pydicer.generate.object import add_object, add_structure_object, add_dose_object +from pydicer.generate.object import ( + add_object, + add_image_object, + add_structure_object, + add_dose_object, +) from pydicer.generate.segmentation import ( read_all_segmentation_logs, segment_image, @@ -235,6 +240,12 @@ def add_object( # pylint: disable=missing-function-docstring ) -> pd.DataFrame: return add_object(self.working_directory, *args, **kwargs) + @copy_doc(add_image_object, remove_args=["working_directory"]) + def add_image_object( # pylint: disable=missing-function-docstring + self, *args, **kwargs + ) -> pd.DataFrame: + return add_image_object(self.working_directory, *args, **kwargs) + @copy_doc(add_structure_object, remove_args=["working_directory"]) def add_structure_object( # pylint: disable=missing-function-docstring self, *args, **kwargs diff --git a/pydicer/visualise/data.py b/pydicer/visualise/data.py index ff469f6..9d2ac47 100644 --- a/pydicer/visualise/data.py +++ b/pydicer/visualise/data.py @@ -56,9 +56,14 @@ def visualise(self, dataset_name=CONVERTED_DIR_NAME, patient=None, force=True): df_process = df_process[df_process.modality.isin(visualise_modalities)] for _, row in get_iterator( - df_process.iterrows(), length=len(df_process), unit="objects", name="visualise" + df_process.iterrows(), + length=len(df_process), + unit="objects", + name="visualise", ): - patient_logger = PatientLogger(row.patient_id, self.output_directory, force=False) + patient_logger = PatientLogger( + row.patient_id, self.output_directory, force=False + ) if row.modality == "CT": img_path = Path(row.path) @@ -90,14 +95,33 @@ def visualise(self, dataset_name=CONVERTED_DIR_NAME, patient=None, force=True): fs = 9 # insert metadata information + patient_id_text = "" + if "PatientID" in ds_dict: + patient_id_text = f"Patient ID: {ds_dict.PatientID}" + + series_instance_uid_text = "" + if "SeriesInstanceUID" in ds_dict: + series_instance_uid_text = ( + f"Series Instance UID: {ds_dict.SeriesInstanceUID}" + ) + + study_description_text = "" + if "StudyDescription" in ds_dict: + study_description_text = ( + f"Study Description: {ds_dict.StudyDescription}" + ) + + study_date_text = "" + if "StudyDate" in ds_dict: + study_date_text = f"Study Description: {ds_dict.StudyDate}" + ax.text( x=0.02, y=0.90, - s=f"Patient ID: {ds_dict.PatientID}\n" - f"Series Instance UID: \n" - f"{ds_dict.SeriesInstanceUID}\n" - f"Study Description: {ds_dict.StudyDescription}\n" - f"Study Date: {ds_dict.StudyDate}", + s=f"{patient_id_text}\n" + f"{series_instance_uid_text}\n" + f"{study_description_text}\n" + f"{study_date_text}", color="black", ha="left", va="top", @@ -164,7 +188,9 @@ def visualise(self, dataset_name=CONVERTED_DIR_NAME, patient=None, force=True): logger.info("Visualisation already exists at %s", vis_filename) continue - img = sitk.ReadImage(str(img_path.joinpath(f"{img_row.modality}.nii.gz"))) + img = sitk.ReadImage( + str(img_path.joinpath(f"{img_row.modality}.nii.gz")) + ) vis = ImageVisualiser(img) masks = { @@ -189,10 +215,14 @@ def visualise(self, dataset_name=CONVERTED_DIR_NAME, patient=None, force=True): # Next visualise the doses on top of their linked image if row.modality == "RTDOSE": - df_linked_struct = get_structures_linked_to_dose(self.working_directory, row) + df_linked_struct = get_structures_linked_to_dose( + self.working_directory, row + ) if len(df_linked_struct) == 0: - logger.warning("No linked structures found for dose: %s", row.sop_instance_uid) + logger.warning( + "No linked structures found for dose: %s", row.sop_instance_uid + ) dose_path = Path(row.path) dose_file = dose_path.joinpath("RTDOSE.nii.gz") @@ -200,7 +230,8 @@ def visualise(self, dataset_name=CONVERTED_DIR_NAME, patient=None, force=True): for _, struct_row in df_linked_struct.iterrows(): # Find the linked image df_linked_img = df_process[ - df_process["sop_instance_uid"] == struct_row.referenced_sop_instance_uid + df_process["sop_instance_uid"] + == struct_row.referenced_sop_instance_uid ] if len(df_linked_img) == 0: @@ -215,13 +246,19 @@ def visualise(self, dataset_name=CONVERTED_DIR_NAME, patient=None, force=True): img_path = Path(img_row.path) # save image inside dose directory - vis_filename = dose_path.joinpath(f"vis_{struct_row.hashed_uid}.png") + vis_filename = dose_path.joinpath( + f"vis_{struct_row.hashed_uid}.png" + ) if vis_filename.exists() and not force: - logger.info("Visualisation already exists at %s", vis_filename) + logger.info( + "Visualisation already exists at %s", vis_filename + ) continue - img = sitk.ReadImage(str(img_path.joinpath(f"{img_row.modality}.nii.gz"))) + img = sitk.ReadImage( + str(img_path.joinpath(f"{img_row.modality}.nii.gz")) + ) dose_img = sitk.ReadImage(str(dose_file)) dose_img = sitk.Resample(dose_img, img) @@ -241,7 +278,8 @@ def visualise(self, dataset_name=CONVERTED_DIR_NAME, patient=None, force=True): if len(masks) == 0: logger.warning( - "No contours found in structure directory: %s", {struct_dir} + "No contours found in structure directory: %s", + {struct_dir}, ) continue