{"name":"napari_cellseg3d","display_name":"CellSeg3D","visibility":"public","icon":"","categories":[],"schema_version":"0.0.4","on_activate":null,"on_deactivate":null,"contributions":{"commands":[{"id":"napari_cellseg3d.load","title":"Create reviewer","python_name":"napari_cellseg3d.plugins:Reviewer","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari_cellseg3d.help","title":"Create Help","python_name":"napari_cellseg3d.plugins:Helper","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari_cellseg3d.utils","title":"Create utilities","python_name":"napari_cellseg3d.plugins:Utilities","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari_cellseg3d.infer","title":"Create Inference widget","python_name":"napari_cellseg3d.plugins:Inferer","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"napari_cellseg3d.train","title":"Create Trainer widget","python_name":"napari_cellseg3d.plugins:Trainer","short_title":null,"category":null,"icon":null,"enablement":null}],"readers":null,"writers":null,"widgets":[{"command":"napari_cellseg3d.load","display_name":"Labeling","autogenerate":false},{"command":"napari_cellseg3d.infer","display_name":"Inference","autogenerate":false},{"command":"napari_cellseg3d.train","display_name":"Training","autogenerate":false},{"command":"napari_cellseg3d.utils","display_name":"Utilities","autogenerate":false},{"command":"napari_cellseg3d.help","display_name":"Help/About...","autogenerate":false}],"sample_data":null,"themes":null,"menus":{},"submenus":null,"keybindings":null,"configuration":[]},"package_metadata":{"metadata_version":"2.1","name":"napari_cellseg3d","version":"0.2.2","dynamic":null,"platform":null,"supported_platform":null,"summary":"Plugin for cell segmentation in 3D","description":"# CellSeg3D: self-supervised (and supervised) 3D cell segmentation, primarily for mesoSPIM data!\n[](https://www.napari-hub.org/plugins/napari-cellseg3d)\n[](https://pypi.org/project/napari-cellseg3d)\n[](https://pepy.tech/project/napari-cellseg3d)\n[](https://pepy.tech/project/napari-cellseg3d)\n[](https://github.com/AdaptiveMotorControlLab/CellSeg3D/raw/main/LICENSE)\n[](https://codecov.io/gh/AdaptiveMotorControlLab/CellSeg3D)\n![\"Code](\"https://img.shields.io/badge/code%20style-black-000000.svg\")
\n\n![\"cellseg3d](\"https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/838605d0-9723-4e43-83cd-6dbfe4adf36b/cellseg-logo.png?format=1500w\" "\\"cellseg3d\\"")
\n\n\n**A package for 3D cell segmentation with deep learning, including a napari plugin**: training, inference, and data review. In particular, this project was developed for analysis of confocal and mesoSPIM-acquired (cleared tissue + lightsheet) tissue datasets, but is not limited to this type of data. [Check out our preprint for more information!](https://www.biorxiv.org/content/10.1101/2024.05.17.594691v1)\n\n\n\n\n\n## Installation\n\n š» See the [Installation page](https://adaptivemotorcontrollab.github.io/CellSeg3D/welcome.html) in the documentation for detailed instructions.\n\n## Documentation\n\nš Documentation is available at [https://AdaptiveMotorControlLab.github.io/CellSeg3D\n](https://adaptivemotorcontrollab.github.io/CellSeg3D/welcome.html)\n\n\nš For additional examples and how to reproduce our paper figures, see: [https://github.com/C-Achard/cellseg3d-figures](https://github.com/C-Achard/cellseg3d-figures)\n\n## Quick Start\n\n```\npip install napari_cellseg3d\n```\n\nTo use the plugin, please run:\n```\nnapari\n```\nThen go into `Plugins > napari-cellseg3d`, and choose which tool to use.\n\n- **Review (label)**: This module allows you to review your labels, from predictions or manual labeling, and correct them if needed. It then saves the status of each file in a csv, for easier monitoring.\n- **Inference**: This module allows you to use pre-trained segmentation algorithms on volumes to automatically label cells and compute statistics.\n- **Train**: This module allows you to train segmentation algorithms from labeled volumes.\n- **Utilities**: This module allows you to perform several actions like cropping your volumes and labels dynamically, by selecting a fixed size volume and moving it around the image; fragment images into smaller cubes for training; or converting labels from instance to segmentation and the opposite.\n\n## Why use CellSeg3D?\n\nThe strength of our approach is we can match supervised model performance with purely self-supervised learning, meaning users don't need to spend (hundreds) of hours on annotation. Here is a quick look of our key results. TL;DR see panel **f**, which shows that with minmal input data we can outperform supervised models:\n\n\n\n\n**Figure 1. Performance of 3D Semantic and Instance Segmentation Models.**\n**a:** Raw mesoSPIM whole-brain sample, volumes and corresponding ground truth labels from somatosensory (S1) and visual (V1) cortical regions.\n**b:** Evaluation of instance segmentation performance for baseline\nthresholding-only, supervised models: Cellpose, StartDist, SwinUNetR, SegResNet, and our self-supervised model WNet3D over three data subsets.\nF1-score is computed from the Intersection over Union (IoU) with ground truth labels, then averaged. Error bars represent 50% Confidence Intervals\n(CIs).\n**c:** View of 3D instance labels from supervised models, as noted, for visual cortex volume in b evaluation.\n**d:** Illustration of our WNet3D architecture showcasing the dual 3D U-Net structure with our modifications.\n\n\n## News\n\n**New version: v0.2.2**\n\n- v0.2.2:\n - Updated the Colab Notebooks for training and inference\n - New models available in the inference demo notebook\n - CRF optional post-processing adjustments (and pip install directly)\n- v0.2.1:\n - Updated plugin default behaviors across the board to be more readily applicable to demo data\n - Threshold value in inference is now automatically set by default according to performance on demo data on a per-model basis\n - Added a grid search utility to find best thresholds for supervised models\n\n- v0.2.0:\n - Changed project name to \"napari_cellseg3d\" to avoid setuptools deprecation\n - Small API changes for training/inference from a script\n - Some fixes to WandB integration and csv saving after training\n\nPrevious additions:\n\n- v0.1.2: Fixed manifest issue for PyPi\n- Improved training interface\n- Unsupervised model : WNet3D\n - Generate labels directly from raw data!\n - Can be trained in napari directly or in Google Colab\n - Pretrained weights for mesoSPIM whole-brain cell segmentation\n- WandB support (install wandb and login to use automatically when training)\n- Remade and improved documentation\n - Moved to Jupyter Book\n - Dedicated installation page, and working ARM64 install for macOS Silicon users\n- New utilities\n- Many small improvements and many bug fixes\n\n\n\n\n## Requirements\n\n**Compatible with Python 3.8 to 3.10.**\nRequires **[napari]**, **[PyTorch]** and **[MONAI]**.\nCompatible with Windows, MacOS and Linux.\nInstallation should not take more than 30 minutes, depending on your internet connection.\n\nFor PyTorch, please see [the PyTorch website for installation instructions].\n\nA CUDA-capable GPU is not needed but very strongly recommended, especially for training.\n\nIf you get errors from MONAI regarding missing readers, please see [MONAI's optional dependencies] page for instructions on getting the readers required by your images.\n\n### Install note for ARM64 (Silicon) Mac users\n\nTo avoid issues when installing on the ARM64 architecture, please follow these steps.\n\n1) Create a new conda env using the provided conda/napari_CellSeg3D_ARM64.yml file :\n\n git clone https://github.com/AdaptiveMotorControlLab/CellSeg3d.git\n cd CellSeg3d\n conda env create -f conda/napari_CellSeg3D_ARM64.yml\n conda activate napari_CellSeg3D_ARM64\n\n\n2) Install a Qt backend (PySide or PyQt5)\n3) Launch napari, the plugin should be available in the plugins menu.\n\n\n\n## Issues\n\n**Help us make the code better by reporting issues and adding your feature requests!**\n\n\nIf you encounter any problems, please [file an issue] along with a detailed description.\n\n## Testing\n\nYou can generate docs locally by running ``make html`` in the docs/ folder.\n\nBefore testing, install all requirements using ``pip install napari-cellseg3d[test]``.\n\n``pydensecrf`` is also required for testing.\n\nTo run tests locally:\n\n- Locally : run ``pytest napari_cellseg3d\\_tests`` in the plugin folder.\n- Locally with coverage : In the plugin folder, run ``coverage run --source=napari_cellseg3d -m pytest`` then ``coverage xml`` to generate a .xml coverage file.\n- With tox : run ``tox`` in the plugin folder (will simulate tests with several python and OS configs, requires substantial storage space)\n\n## Contributing\n\nContributions are very welcome.\n\nPlease ensure the coverage at least stays the same before you submit a pull request.\n\nFor local installation from Github cloning, please run:\n\n```\npip install -e .\n```\n\n## License\n\nDistributed under the terms of the [MIT] license.\n\n\"napari-cellseg3d\" is free and open source software.\n\n[napari-hub]: https://www.napari-hub.org/plugins/napari-cellseg3d\n\n[file an issue]: https://github.com/AdaptiveMotorControlLab/CellSeg3D/issues\n[napari]: https://github.com/napari/napari\n[Cookiecutter]: https://github.com/audreyr/cookiecutter\n[@napari]: https://github.com/napari\n[MIT]: http://opensource.org/licenses/MIT\n[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin\n[tox]: https://tox.readthedocs.io/en/latest/\n[pip]: https://pypi.org/project/pip/\n[PyPI]: https://pypi.org/\n[Installation page]: https://adaptivemotorcontrollab.github.io/CellSeg3D/source/guides/installation_guide.html\n[the PyTorch website for installation instructions]: https://pytorch.org/get-started/locally/\n[PyTorch]: https://pytorch.org/get-started/locally/\n[MONAI's optional dependencies]: https://docs.monai.io/en/stable/installation.html#installing-the-recommended-dependencies\n[MONAI]: https://docs.monai.io/en/stable/installation.html#installing-the-recommended-dependencies\n\n## Citation\n\n```\n@article {Achard2024,\n\tauthor = {Achard, Cyril and Kousi, Timokleia and Frey, Markus and Vidal, Maxime and Paychere, Yves and Hofmann, Colin and Iqbal, Asim and Hausmann, Sebastien B. and Pages, Stephane and Mathis, Mackenzie W.},\n\ttitle = {CellSeg3D: self-supervised 3D cell segmentation for microscopy},\n\telocation-id = {2024.05.17.594691},\n\tyear = {2024},\n\tdoi = {10.1101/2024.05.17.594691},\n\tpublisher = {Cold Spring Harbor Laboratory},\n\tURL = {https://www.biorxiv.org/content/early/2024/05/17/2024.05.17.594691},\n\teprint = {https://www.biorxiv.org/content/early/2024/05/17/2024.05.17.594691.full.pdf},\n\tjournal = {bioRxiv}\n}\n```\n## Acknowledgements\n\nThis plugin was developed by originally Cyril Achard, Maxime Vidal, Mackenzie Mathis.\nThis work was funded, in part, from the Wyss Center to the [Mathis Laboratory of Adaptive Intelligence](https://www.mackenziemathislab.org/).\nPlease refer to the documentation for full acknowledgements.\n\n## Plugin 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