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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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-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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**Help us make the code better by reporting issues and adding your feature requests!**
💻 See the [Installation page] in the documentation for detailed instructions.
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## Documentation
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📚 A lot of documentation is available at https://AdaptiveMotorControlLab.github.io/CellSeg3d
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----------------------------------
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You can also generate docs by running ``make html`` in the docs/ folder.
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## Quick Start
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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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-**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.
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-**Inference**: This module allows you to use pre-trained segmentation algorithms on volumes to automatically label cells and compute statistics.
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-**Train**: This module allows you to train segmentation algorithms from labeled volumes.
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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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## News
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Previous additions :
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- Improved training interface
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- Unsupervised model : WNet
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- Generate labels directly from raw data!
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- Can be trained in napari directly or in Colab
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- Unsupervised model : WNet3D
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- Generate labels directly from raw data!
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- Can be trained in napari directly or in Google Colab
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- Pretrained weights for mesoSPIM whole-brain cell segmentation
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- WandB support (install wandb and login to use automatically when training)
See the [Installation page] in the documentation for detailed instructions.
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### M1 Mac users
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### Install note for M1/M2 Mac users
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To avoid issues when installing on the ARM64 architecture, please follow these steps.
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OR directly via [napari-hub] (see Installation section above)
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## Documentation
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Available at https://AdaptiveMotorControlLab.github.io/CellSeg3d
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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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-**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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-**Inference**: This module allows you to use pre-trained segmentation algorithms on volumes to automatically label cells and compute statistics.
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-**Train**: This module allows you to train segmentation algorithms from labeled volumes.
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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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## Requirements
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## Issues
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**Help us make the code better by reporting issues and adding your feature requests!**
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If you encounter any problems, please [file an issue] along with a detailed description.
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## Testing
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## Acknowledgements
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This plugin was developed by Cyril Achard, Maxime Vidal, Mackenzie Mathis.
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This work was funded, in part, from the Wyss Center to the [Mathis Laboratory of Adaptive Motor Control](https://www.mackenziemathislab.org/).
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This plugin was developed by originally Cyril Achard, Maxime Vidal, Mackenzie Mathis.
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This work was funded, in part, from the Wyss Center to the [Mathis Laboratory of Adaptive Intelligence](https://www.mackenziemathislab.org/).
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Please refer to the documentation for full acknowledgements.
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