Cellpose
A generalist algorithm for cellular segmentation with human-in-the-loop training and superhuman generalization across diverse microscopy images.
What is Cellpose?
Cellpose is a deep learning-based algorithm for automatically segmenting cells and nuclei in microscopy images. It solves the problem of accurately identifying cellular boundaries across diverse experimental conditions, such as different stains, noise levels, and imaging modalities, which traditionally required manual tuning or custom models. The tool provides pre-trained models that work out-of-the-box and can be fine-tuned with user data.
Biologists, bioimage analysts, and computational researchers working with microscopy data who need to quantify cellular morphology, count cells, or analyze tissue structures in 2D or 3D images.
Developers choose Cellpose for its exceptional generalization across image types, reducing the need for manual annotation, and its integrated human-in-the-loop training that allows continuous model improvement with minimal labeled data. Its open-source nature and active community support make it a versatile alternative to commercial segmentation software.
Overview
a generalist algorithm for cellular segmentation with human-in-the-loop capabilities
Use Cases
Best For
- Segmenting cells in fluorescence microscopy images with variable staining
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