cellpose-cell-segmentation

Automate cell segmentation in microscopy images by applying pre-trained deep learning models to identify cells and nuclei. This approach handles varying image conditions and touching cells, generating label masks for downstream morphology analysis and tracking.

Cellpose works with both brightfield and fluorescence images without requiring retraining. The pre-trained models generalize across diverse cell types, magnifications, and staining conditions to perform robust cell and nucleus identification.

You can use scikit-image for morphology analysis after cell segmentation because the output generates compatible label masks. These masks integrate directly with scikit-image for measurements and TrackPy for tracking in downstream bioimage analysis workflows.

The best way to segment touching cells in high-throughput fluorescence microscopy is using deep learning-based segmentation with pre-trained models. This method automatically segments thousands of cells, overcoming traditional limitations when handling varying image conditions.

You need numpy and matplotlib installed along with cellpose and scikit-image to run deep learning cell segmentation. These Python packages are required dependencies for handling image processing and generating segmentation masks.