{"name":"napari-cellseg3d","display_name":"Cell Segmentation Annotator","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":"Review","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":"0.0.2rc6","dynamic":null,"platform":null,"supported_platform":null,"summary":"plugin for cell segmentation","description":"# napari-cellseg3D: a napari plug-in for direct 3D cell segmentation with deep learning\n\n\n\"cellseg3d\n\n\"Code\n[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/AdaptiveMotorControlLab/CellSeg3d/raw/main/LICENSE)\n[![PyPI](https://img.shields.io/pypi/v/napari-cellseg3d.svg?color=green)](https://pypi.org/project/napari-cellseg3d)\n[![Python Version](https://img.shields.io/pypi/pyversions/napari-cellseg-annotator.svg?color=green)](https://python.org)\n[![codecov](https://codecov.io/gh/AdaptiveMotorControlLab/CellSeg3d/branch/main/graph/badge.svg?token=hzUcn3XN8F)](https://codecov.io/gh/AdaptiveMotorControlLab/CellSeg3d)\n[![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/napari-cellseg3d)](https://www.napari-hub.org/plugins/napari-cellseg3d)\n\n\nA napari plugin for 3D cell segmentation: training, inference, and data review. In particular, this project was developed for analysis of mesoSPIM-acquired (cleared tissue + lightsheet) datasets.\n\n----------------------------------\n\n## News\n\n**June 2022: This is an alpha version, please expect bugs and issues, and help us make the code better by reporting them as an issue!**\n\n\n\n## Installation\n\nNote : we recommend using conda to create a new environment for the plugin.\n\n conda create --name python=3.8 napari-cellseg3d\n conda activate napari-cellseg3d\n\nYou can install `napari-cellseg3d` via [pip]: \n\n pip install napari-cellseg3d\n\nOR directly via [napari-hub]:\n\n- Install napari from pip with `pip install \"napari[all]\"`,\nthen from the “Plugins” menu within the napari application, select “Install/Uninstall Package(s)...”\n- Copy `napari-cellseg3d` and paste it where it says “Install by name/url…”\n- Click “Install”\n\n## Documentation\n\nAvailable at https://AdaptiveMotorControlLab.github.io/CellSeg3d\n\nYou can also generate docs by running ``make html`` in the docs folder.\n\n## Usage\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**: 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; computing prediction scores from ground truth and predicition labels; or converting labels from instance to segmentation and the opposite.\n\n\n## Requirements\n**Python >= 3.8 required**\n\nRequires **pytorch** and **MONAI**.\nFor PyTorch, please see [PyTorch's website for installation instructions].\nA CUDA-capable GPU is not needed but very strongly recommended, especially for training.\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\n## Issues\n\nIf you encounter any problems, please [file an issue] along with a detailed description.\n\n\n## Testing\n\nTo run tests locally:\n\n- Locally : run ``pytest`` 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[BSD-3]: http://opensource.org/licenses/BSD-3-Clause\n[GNU GPL v3.0]: http://www.gnu.org/licenses/gpl-3.0.txt\n[GNU LGPL v3.0]: http://www.gnu.org/licenses/lgpl-3.0.txt\n[Apache Software License 2.0]: http://www.apache.org/licenses/LICENSE-2.0\n[Mozilla Public License 2.0]: https://www.mozilla.org/media/MPL/2.0/index.txt\n[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin\n\n[napari]: https://github.com/napari/napari\n[tox]: https://tox.readthedocs.io/en/latest/\n[pip]: https://pypi.org/project/pip/\n[PyPI]: https://pypi.org/\n\n[PyTorch's website for installation instructions]: https://pytorch.org/get-started/locally/\n[MONAI's optional dependencies]: https://docs.monai.io/en/stable/installation.html#installing-the-recommended-dependencies\n\n## Acknowledgements\n\nThis plugin was developed by Cyril Achard, Maxime Vidal, Mackenzie Mathis. This work was funded, in part, from the Wyss Center to the [Mathis Laboratory of Adaptive Motor Control](https://www.mackenziemathislab.org/).\n\n\n## Plugin base\nThis [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template.\n\n\n","description_content_type":"text/markdown","keywords":null,"home_page":"https://github.com/AdaptiveMotorControlLab/CellSeg3d","download_url":null,"author":"Cyril Achard, Maxime Vidal, Jessy Lauer, Mackenzie Mathis","author_email":"Cyril Achard , Maxime Vidal , Mackenzie Mathis ","maintainer":null,"maintainer_email":null,"license":"MIT","classifier":["Development Status :: 2 - Pre-Alpha","Intended Audience :: Science/Research","Framework :: napari","Topic :: Software Development :: Testing","Programming Language :: Python","Programming Language :: Python :: 3","Programming Language :: Python :: 3.8","Programming Language :: Python :: 3.9","Programming Language :: Python :: 3.10","Operating System :: OS Independent","License :: OSI Approved :: MIT License","Topic :: Scientific/Engineering :: Artificial Intelligence","Topic :: Scientific/Engineering :: Image Processing","Topic :: Scientific/Engineering :: Visualization"],"requires_dist":["numpy","napari[all] (>=0.4.14)","QtPy","opencv-python (>=4.5.5)","dask-image (>=0.6.0)","scikit-image (>=0.19.2)","matplotlib (>=3.4.1)","tifffile (>=2022.2.9)","imageio-ffmpeg (>=0.4.5)","torch (>=1.11)","monai[einops,nibabel] (>=0.9.0)","itk","tqdm","nibabel","scikit-image","pillow","matplotlib","vispy (>=0.9.6)","isort ; extra == 'dev'","black ; extra == 'dev'","ruff ; extra == 'dev'","sphinx ; extra == 'docs'","sphinx-autodoc-typehints ; extra == 'docs'","sphinx-rtd-theme ; extra == 'docs'","twine ; extra == 'docs'","pytest ; extra == 'test'","pytest-qt ; extra == 'test'","coverage ; extra == 'test'","tox ; extra == 'test'","twine ; extra == 'test'"],"requires_python":">=3.8","requires_external":null,"project_url":["Bug Tracker, https://github.com/AdaptiveMotorControlLab/CellSeg3d/issues","Documentation, https://adaptivemotorcontrollab.github.io/cellseg3d-docs/res/welcome.html","Source Code, https://github.com/AdaptiveMotorControlLab/CellSeg3d"],"provides_extra":["dev","docs","test"],"provides_dist":null,"obsoletes_dist":null},"npe1_shim":false}