https://github.com/vanvalenlab/cellSAM

Codebase for "A Foundation Model for Cell Segmentation"
https://github.com/vanvalenlab/cellSAM

Last synced: 2 months ago
JSON representation

Codebase for "A Foundation Model for Cell Segmentation"

• Host: GitHub
• URL: https://github.com/vanvalenlab/cellSAM
• Owner: vanvalenlab
• License: other
• Created: 2024-02-14T22:41:18.000Z (11 months ago)
• Default Branch: master
• Last Pushed: 2024-08-02T02:02:25.000Z (5 months ago)
• Last Synced: 2024-11-09T14:14:52.754Z (2 months ago)
• Language: Jupyter Notebook
• Size: 2.29 MB
• Stars: 51
• Watchers: 4
• Forks: 8
• Open Issues: 17
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

Awesome Lists containing this project

• awesome-biological-image-analysis - CellSAM - A foundation model for cell segmentation trained on a diverse range of cells and data types. (Image processing and segmentation)

README

        
# CellSAM: A Foundation Model for Cell Segmentation

[  

](https://cellsam.deepcell.org/)

## Description

This repository provides inference code for CellSAM. CellSAM is described in more detail in the [preprint](https://www.biorxiv.org/content/10.1101/2023.11.17.567630v3), and is publicly deployed at [cellsam.deepcell.org](https://cellsam.deepcell.org/). CellSAM achieves state-of-the-art performance on segmentation across a variety of cellular targets (bacteria, tissue, yeast, cell culture, etc.) and imaging modalities (brightfield, fluorescence, phase, etc.). Feel free to [reach out](mailto:[email protected]) for support/questions! The full dataset used to train CellSAM is available [here](https://storage.googleapis.com/cellsam-data/dataset.tar.gz).

## Getting started

The easiest way to get started with CellSAM is with pip

`pip install git+https://github.com/vanvalenlab/cellSAM.git`

CellSAM requires `python>=3.10`, but otherwise uses pure PyTorch. A sample image is included in this repository. Segmentation can be performed as follows

```

import numpy as np

from cellSAM import segment_cellular_image

img = np.load("sample_imgs/yeaz.npy")

mask, _, _ = segment_cellular_image(img, device='cuda')

```

For more details, see `cellsam_introduction.ipynb`.

### Napari package

CellSAM includes a basic napari package for annotation functionality. To install the additional napari dependencies, use pip.

`pip install git+https://github.com/vanvalenlab/cellSAM.git#egg=cellsam[napari]`

To launch the napari app, run `cellsam napari`. 

## Citation

Please cite us if you use CellSAM.

```

@article{israel2023foundation,

  title={A Foundation Model for Cell Segmentation},

  author={Israel, Uriah and Marks, Markus and Dilip, Rohit and Li, Qilin and Schwartz, Morgan and Pradhan, Elora and Pao, Edward and Li, Shenyi and Pearson-Goulart, Alexander and Perona, Pietro and others},

  journal={bioRxiv},

  publisher={Cold Spring Harbor Laboratory Preprints},

  doi = {10.1101/2023.11.17.567630},

}

```