cellpose-cell-segmentationlisted

# Cellpose — Deep Learning Cell Segmentation ## Overview Cellpose uses a flow-based neural network to segment individual cells or nuclei in fluorescence microscopy images without manual parameter tuning. Pre-trained models (`cyto3`, `nuclei`, `tissuenet`) generalize across cell types, magnifications, and staining conditions — eliminating the need for manual threshold selection or watershed parameter optimization. Cellpose outputs integer label masks (each cell = unique integer) compatible with scikit-image `regionprops` for morphology measurement and with TrackPy for tracking. A built-in diameter estimator removes the need to specify cell size, though providing an approximate diameter improves accuracy. ## When to Use - Segmenting cells or nuclei in fluorescence microscopy images where rule-based thresholding fails due to varying intensity or cell touching - Processing large microscopy datasets in batch without per-image parameter tuning - Segmenting diverse cell types (adherent cells, blood cells, bacteria, organoids) with a single model - Producing label masks for downstream region property measurement (area, intensity, shape) with scikit-image - 3D volumetric segmentation of z-stack microscopy data with `do_3D=True` - Use **scikit-image watershed** when cells are well-separated and rule-based thresholding is sufficient - Use **StarDist** as an alternative deep learning segmenter optimized for star-convex cells (neurons, nuclei) ## Prerequisites - **Python packages**: