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empanada-napari.json
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empanada-napari.json
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{
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{
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{
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{
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},
{
"id": "empanada-napari.label_counter_widget",
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{
"id": "empanada-napari.merge_labels",
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},
{
"id": "empanada-napari.delete_labels",
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},
{
"id": "empanada-napari.morph_labels",
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{
"id": "empanada-napari.split_labels",
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{
"id": "empanada-napari.filter_small_labels",
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{
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{
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},
{
"id": "empanada-napari.pick_patches",
"title": "Pick finetune/training patches",
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},
{
"id": "empanada-napari.store_dataset",
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},
{
"id": "empanada-napari.finetuning_widget",
"title": "Finetune a model",
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},
{
"id": "empanada-napari.training_widget",
"title": "Train a model",
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},
{
"id": "empanada-napari.register_model_widget",
"title": "Register a model",
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},
{
"id": "empanada-napari.get_info_widget",
"title": "Get model info",
"python_name": "empanada_napari._finetune:get_info_widget",
"short_title": null,
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"icon": null,
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},
{
"id": "empanada-napari.export_model_widget",
"title": "Export a model",
"python_name": "empanada_napari._export_import_models:export_model_widget",
"short_title": null,
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"icon": null,
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},
{
"id": "empanada-napari.import_model_widget",
"title": "Import a model",
"python_name": "empanada_napari._export_import_models:import_model_widget",
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}
],
"readers": null,
"writers": null,
"widgets": [
{
"command": "empanada-napari.test_widget",
"display_name": "2D Inference (Parameter Testing)",
"autogenerate": false
},
{
"command": "empanada-napari.volume_inference_widget",
"display_name": "3D Inference",
"autogenerate": false
},
{
"command": "empanada-napari.export_batch_segs",
"display_name": "Export Segmentations",
"autogenerate": false
},
{
"command": "empanada-napari.label_counter_widget",
"display_name": "Count Labels",
"autogenerate": false
},
{
"command": "empanada-napari.merge_labels",
"display_name": "Merge Labels",
"autogenerate": false
},
{
"command": "empanada-napari.delete_labels",
"display_name": "Delete Labels",
"autogenerate": false
},
{
"command": "empanada-napari.morph_labels",
"display_name": "Morph Labels",
"autogenerate": false
},
{
"command": "empanada-napari.split_labels",
"display_name": "Split Labels",
"autogenerate": false
},
{
"command": "empanada-napari.filter_small_labels",
"display_name": "Filter Labels",
"autogenerate": false
},
{
"command": "empanada-napari.jump_to_label",
"display_name": "Jump to label",
"autogenerate": false
},
{
"command": "empanada-napari.find_next_available_label",
"display_name": "Find next available label",
"autogenerate": false
},
{
"command": "empanada-napari.pick_patches",
"display_name": "Pick finetune/training patches",
"autogenerate": false
},
{
"command": "empanada-napari.store_dataset",
"display_name": "Save finetune/training patches",
"autogenerate": false
},
{
"command": "empanada-napari.finetuning_widget",
"display_name": "Finetune a model",
"autogenerate": false
},
{
"command": "empanada-napari.training_widget",
"display_name": "Train a model",
"autogenerate": false
},
{
"command": "empanada-napari.register_model_widget",
"display_name": "Register a model",
"autogenerate": false
},
{
"command": "empanada-napari.get_info_widget",
"display_name": "Get model info",
"autogenerate": false
},
{
"command": "empanada-napari.export_model_widget",
"display_name": "Export a model",
"autogenerate": false
},
{
"command": "empanada-napari.import_model_widget",
"display_name": "Import a model",
"autogenerate": false
}
],
"sample_data": null,
"themes": null,
"menus": {},
"submenus": null,
"keybindings": null,
"configuration": []
},
"package_metadata": {
"metadata_version": "2.1",
"name": "empanada-napari",
"version": "1.1.1",
"dynamic": null,
"platform": null,
"supported_platform": null,
"summary": "Napari plugin of algorithms for Panoptic Segmentation of organelles in EM",
"description": "# empanada-napari\n\n> [!IMPORTANT]\n> **New Version Announcement!**\n> * New modules \n> * Morph labels - applies morphological operations to labels\n> * Count labels - counts and lists the label IDs within the dataset\n> * Filter labels - removes small pixel/voxel area labels or labels touching the image boundaries\n> * Export and import a model - export or import locally saved model files to use within empanada-napari\n> * Updated modules \n> * Export segmentations - now allows 3D segmentations to be exported as a single .tiff image\n> * Pick and save finetune/training patches - now allows paired grayscale and label mask images to create training patches \n> * Split label - now allows users to specify new label IDs \n> * Updated documentation\n> * Check out the updated documentation [here](https://empanada.readthedocs.io/en/latest/index.html)!\n\n**The paper describing this work is now available [on Cell Systems](https://www.cell.com/cell-systems/fulltext/S2405-4712(22)00494-X).**\n\n**Documentation for the plugin, including more detailed installation instructions, can be found [here](https://empanada.readthedocs.io/en/latest/empanada-napari.html).**\n\nempanada is a tool for deep learning-based panoptic segmentation of 2D and 3D electron microscopy images of cells.\nThis plugin allows the running of panoptic segmentation models trained in empanada within [napari](https://napari.org).\nFor help with this plugin please open an [issue](https://github.com/volume-em/empanada-napari/issues), for issues with napari specifically\nraise an [issue here instead](https://github.com/napari/napari/issues).\n\n## Implemented Models\n\n - *MitoNet*: A generalist mitochondrial instance segmentation model.\n\n## Example Datasets\n\nVolume EM datasets for benchmarking mitochondrial instance segmentation are available from\n[EMPIAR-10982](https://www.ebi.ac.uk/empiar/EMPIAR-10982/).\n\n## Installation\n\n### New Users\n\nIf you've previously installed and used conda, it's recommended (but optional) to create a new virtual \nenvironment in order to avoid dependency conflicts. \n\nempanada-napari works with python=3.9 or lower\n\nIt's recommended to have installed napari through [conda](https://docs.conda.io/en/latest/miniconda.html). Then to install this plugin:\n\n```shell\npip install empanada-napari==1.1.1\n```\n\nLaunch napari:\n\n```shell\nnapari\n```\n\nLook for empanada-napari under the \"Plugins\" menu.\n\n\n### Returning Users\n\nIf you installed napari into a virtual environment as suggested in the original release documentation, \nbe sure to activate it and uninstall the old empanada-napari.\n\n```shell\npip uninstall empanada-napari\n```\n\nThen install the newest version:\n\n```shell\npip install empanada-napari==1.1.1\n```\n\n\n\n\n## GPU Support\n\n**Note: Mac doesn't support NVIDIA GPUS. This section only applies to Windows and Linux systems.**\n\nAs for any deep learning models, having a GPU installed on your system will significantly\nincrease model throughput (although we ship CPU optimized versions of all models with the plugin).\n\nThis plugin relies on torch for running models. If a GPU was found on your system, then you will see that the\n\"Use GPU\" checkbox is checked by default in the \"2D Inference\" and \"3D Inference\" plugin widgets. Or if when running\ninference you see a message that says \"Using CPU\" in the terminal that means a GPU is not being used.\n\nMake sure that GPU drivers are correctly installed. In terminal or command prompt:\n\n```shell\nnvidia-smi\n```\n\nIf this returns \"command not found\" then you need to [install the driver from NVIDIA](https://www.nvidia.com/download/index.aspx). Instead, if\nif the driver is installed correctly, you may need to switch to the GPU enabled version of torch.\n\nFirst, uninstall the current version of torch:\n\n```shell\npip uninstall torch\n```\n\nThen [install torch >= 1.10 using conda for your system](https://pytorch.org/get-started/locally/).\nThis command should work:\n\n```shell\nconda install pytorch cudatoolkit=11.3 -c pytorch\n```\n\n## Citing this work\n\nIf you use results generated by this plugin in a publication, please cite:\n\n```bibtex\n@article { Conrad2023,\n author = {Conrad, Ryan and Narayan, Kedar},\n title = {Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model trained on a diverse dataset},\n journal = {Cell Systems},\n year = {2023},\n month = {Jan},\n day = {18},\n publisher = {Elsevier},\n volume = {14},\n number = {1},\n pages = {58-71.e5},\n issn = {2405-4712},\n doi = {10.1016/j.cels.2022.12.006},\n url = {https://doi.org/10.1016/j.cels.2022.12.006}\n}\n```\n",
"description_content_type": "text/markdown",
"keywords": null,
"home_page": "https://github.com/volume-em/empanada-napari",
"download_url": null,
"author": "Madeline Barry, Abhishek Bhardwaj, Ryan Conrad",
"author_email": "[email protected]",
"maintainer": null,
"maintainer_email": null,
"license": "BSD-3-Clause",
"classifier": [
"Development Status :: 2 - Pre-Alpha",
"Intended Audience :: Developers",
"Framework :: napari",
"Topic :: Software Development :: Testing",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Operating System :: OS Independent",
"License :: OSI Approved :: BSD License"
],
"requires_dist": [
"opencv-python ==4.9.0.80",
"opencv-python-headless ==4.9.0.80",
"napari ==0.4.18",
"numpy ==1.22",
"napari-plugin-engine >=0.1.4",
"scikit-image >=0.19",
"empanada-dl >=0.1.7",
"numba ==0.59.0",
"imagecodecs",
"openpyxl",
"imagehash",
"mlflow",
"simpleitk",
"tqdm"
],
"requires_python": ">=3.7",
"requires_external": null,
"project_url": [
"Bug Tracker, https://github.com/volume-em/empanada-napari/issues",
"Documentation, https://github.com/volume-em/empanada-napari#README.md",
"Source Code, https://github.com/volume-em/empanada-napari",
"User Support, https://github.com/volume-em/empanada-napari/issues"
],
"provides_extra": null,
"provides_dist": null,
"obsoletes_dist": null
},
"npe1_shim": false
}