Plugin for cell segmentation in 3D
napari-cellseg3D: a napari plug-in for direct 3D cell segmentation with deep learning
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.
Help us make the code better by reporting issues and adding your feature requests!
New version : v0.1.1
- Improved training interface
- Unsupervised model : WNet
- Generate labels directly from raw data !
- Can be trained in napari directly or in Colab
- Pretrained weights for mesoSPIM whole-brain cell segmentation
- WandB support (install wandb and login to use automatically when training)
- Remade and improved documentation
- Moved to Jupyter Book
- Dedicated installation page, and working ARM64 install for macOS Silicon users
- New utilities
- Many small improvements and many bug fixes
Note : we recommend using conda to create a new environment for the plugin. M1 Mac users, please see the M1 install section
conda create --name napari-cellseg3d python=3.8
conda activate napari-cellseg3d
You can install
napari-cellseg3d via pip:
pip install napari-cellseg3d[all]
OR directly via napari-hub:
- Install napari from pip with
pip install "napari[all]", then from the “Plugins” menu within the napari application, select “Install/Uninstall Package(s)...”
napari-cellseg3dand paste it where it says “Install by name/url…”
- Click “Install”
- Restart napari
M1 Mac users
To avoid issues when installing on the ARM64 architecture, please follow these steps.
Create a new conda env using the provided conda/napari_cellseg3d_m1.yml file :
git clone https://github.com/AdaptiveMotorControlLab/CellSeg3d.git cd CellSeg3d conda env create -f conda/napari_cellseg3d_m1.yml conda activate napari_cellseg3d_m1
Install the plugin. From repository root folder, run :
pip install -e .
OR directly via PyPi :
pip install napari-cellseg3d
OR directly via napari-hub (see Installation section above)
Available at https://AdaptiveMotorControlLab.github.io/CellSeg3d
You can also generate docs by running
make html in the docs/ folder.
To use the plugin, please run:
Then go into Plugins > napari-cellseg3d, and choose which tool to use.
- 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.
- Inference: This module allows you to use pre-trained segmentation algorithms on volumes to automatically label cells and compute statistics.
- Train: This module allows you to train segmentation algorithms from labeled volumes.
- 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.
For PyTorch, please see the PyTorch website for installation instructions.
A CUDA-capable GPU is not needed but very strongly recommended, especially for training.
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.
If you encounter any problems, please file an issue along with a detailed description.
To run tests locally:
- Locally : run
pytestin the plugin folder
- Locally with coverage : In the plugin folder, run
coverage run --source=napari_cellseg3d -m pytestthen
coverage xmlto generate a .xml coverage file.
- With tox : run
toxin the plugin folder (will simulate tests with several python and OS configs, requires substantial storage space)
Contributions are very welcome.
Please ensure the coverage at least stays the same before you submit a pull request.
For local installation from Github cloning, please run:
pip install -e .
Distributed under the terms of the MIT license.
"napari-cellseg3d" is free and open source software.
This 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. Please refer to the documentation for full acknowledgements.
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