CellSAM: A Foundation Model for Cell Segmentation

Description

This repository provides inference code for CellSAM. CellSAM is described in more detail in the preprint, and is publicly deployed at 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 for support/questions! The full dataset used to train CellSAM is available here.

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},
}