CellSAM
A foundation model for cell segmentation that achieves state-of-the-art performance across diverse cellular targets and imaging modalities.
What is CellSAM?
CellSAM is a foundation model for cell segmentation that provides inference code for segmenting cellular images from various biological samples and imaging modalities. It solves the problem of accurately identifying and delineating individual cells in microscopy images, which is crucial for quantitative biological analysis. The model achieves state-of-the-art performance across diverse cellular targets including bacteria, tissue, yeast, and cell culture.
Researchers and scientists working with biological microscopy data who need to segment cells for quantitative analysis, particularly in fields like cell biology, microbiology, and medical imaging.
Developers choose CellSAM because it provides a single, versatile model that works across multiple imaging modalities and cellular targets without requiring modality-specific tuning. Its state-of-the-art performance and easy-to-use Python API make it accessible for both research and production applications.
Overview
Codebase for "A Foundation Model for Cell Segmentation"
Use Cases
Best For
- Segmenting cells in brightfield microscopy images
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
