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73 changes: 37 additions & 36 deletions README.md
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# napari-cellseg3D: a napari plug-in for direct 3D cell segmentation with deep learning

# CellSeg3D: self-supervised (and supervised) 3D cell segmentation for lightsheet microscopy
<img src="https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/838605d0-9723-4e43-83cd-6dbfe4adf36b/cellseg-logo.png?format=1500w" title="cellseg3d" alt="cellseg3d logo" width="350" align="right" vspace = "80"/>

<a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
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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.
- 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!**
![demo](https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/0d16a71b-3ff2-477a-9d83-18d96cb1ce28/full_demo.gif?format=500w)

## Installation

💻 See the [Installation page] in the documentation for detailed instructions.

## Documentation

📚 A lot of documentation is available at https://AdaptiveMotorControlLab.github.io/CellSeg3d

----------------------------------
You can also generate docs by running ``make html`` in the docs/ folder.

## Quick Start

To use the plugin, please run:
```
napari
```
Then go into Plugins > napari-cellseg3d, and choose which tool to use.

- **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.
- **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.

## News

Expand All @@ -24,9 +46,9 @@ A napari plugin for 3D cell segmentation: training, inference, and data review.
Previous additions :

- Improved training interface
- Unsupervised model : WNet
- Generate labels directly from raw data !
- Can be trained in napari directly or in Colab
- Unsupervised model : WNet3D
- Generate labels directly from raw data!
- Can be trained in napari directly or in Google Colab
- Pretrained weights for mesoSPIM whole-brain cell segmentation
- WandB support (install wandb and login to use automatically when training)
- Remade and improved documentation
Expand All @@ -35,15 +57,9 @@ Previous additions :
- New utilities
- Many small improvements and many bug fixes

## Demo

![demo](https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/0d16a71b-3ff2-477a-9d83-18d96cb1ce28/full_demo.gif?format=500w)

## Installation

See the [Installation page] in the documentation for detailed instructions.

### M1 Mac users
### Install note for M1/M2 Mac users

To avoid issues when installing on the ARM64 architecture, please follow these steps.

Expand All @@ -64,24 +80,6 @@ To avoid issues when installing on the ARM64 architecture, please follow these s

OR directly via [napari-hub] (see Installation section above)

## Documentation

Available at https://AdaptiveMotorControlLab.github.io/CellSeg3d

You can also generate docs by running ``make html`` in the docs/ folder.

## Usage

To use the plugin, please run:
```
napari
```
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.

## Requirements

Expand All @@ -96,6 +94,9 @@ If you get errors from MONAI regarding missing readers, please see [MONAI's opti

## Issues

**Help us make the code better by reporting issues and adding your feature requests!**


If you encounter any problems, please [file an issue] along with a detailed description.

## Testing
Expand Down Expand Up @@ -147,8 +148,8 @@ Distributed under the terms of the [MIT] license.

## Acknowledgements

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](https://www.mackenziemathislab.org/).
This plugin was developed by originally Cyril Achard, Maxime Vidal, Mackenzie Mathis.
This work was funded, in part, from the Wyss Center to the [Mathis Laboratory of Adaptive Intelligence](https://www.mackenziemathislab.org/).
Please refer to the documentation for full acknowledgements.

## Plugin base
Expand Down