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Requires manual installation of **pytorch** and **MONAI**.
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For Pytorch, please see [PyTorch's website for installation instructions].
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A CUDA-capable GPU is not needed but very strongly recommended, especially for training.
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A 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.
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If 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.
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----------------------------------
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## Installation
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You can install `napari-cellseg3d` via [pip]:
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You can install `napari-cellseg-3d` via [pip]:
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pip install napari-cellseg3d
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pip install napari-cellseg-3d
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For local installation, please run:
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## Documentation
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```
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pip install -e .
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```
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Available on the [Github pages website](https://adaptivemotorcontrollab.github.io/cellseg3d-docs/)
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## Documentation
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Source files can be found at https://AdaptiveMotorControlLab.github.io/cellseg3d-docs
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You can generate docs by running ``make html`` in the *docs* folder
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You can also generate docs by running ``make html`` in the docs folder.
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## Usage
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To use the plugin, please run:
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```
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napari
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```
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Then go into Plugins > napari-cellseg3d, and choose which tool to use.
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Then go into Plugins > napari-cellseg-3d, and choose which tool to use.
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-**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.
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-**Infer**: This module allows you to use pre-trained segmentation algorithms on volumes to automatically label cells.
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-**Train**: This module allows you to train segmentation algorithms from labeled volumes.
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-**Crop utility**: This module allows you to crop your volumes and labels dynamically, by selecting a fixed size volume and moving it around the image.
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-**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.
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## Testing
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To run tests locally:
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- Locally : run ``pytest`` in the plugin folder
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- Locally with coverage : In the plugin folder, run ``coverage run --source=napari_cellseg3d -m pytest`` then ``coverage.xml`` to generate a .xml coverage file.
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- Locally with coverage : In the plugin folder, run ``coverage run --source=src -m pytest`` then ``coverage.xml`` to generate a .xml coverage file.
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- With tox : run ``tox`` in the plugin folder (will simulate tests with several python and OS configs, requires substantial storage space)
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## Contributing
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Contributions are very welcome. Tests can be run with [tox], please ensure
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the coverage at least stays the same before you submit a pull request.
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Contributions are very welcome.
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Please ensure the coverage at least stays the same before you submit a pull request.
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For local installation, please run:
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```
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pip install -e .
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```
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## License
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Distributed under the terms of the [MIT] license,
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"napari-cellseg3d" is free and open source software
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"napari-cellseg-3d" is free and open source software
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## Issues
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If you encounter any problems, please [file an issue] along with a detailed description.
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## Requirements
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**Python >= 3.8 required**
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Requires manual installation of **pytorch** and **MONAI**.
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For Pytorch, please see [PyTorch's website for installation instructions].
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A CUDA-capable GPU is not needed but very strongly recommended, especially for training.
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If 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.
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[[file an issue]: https://github.com/AdaptiveMotorControlLab/CellSeg3d/issues
This plugin was developed by Cyril Achard & Maxime Vidal.
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This [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template. This work was funded, in part, from the Wyss Center to the Adaptive Motor Control Lab.
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