Cellpose is an open-source deep-learning cell segmentation tool developed at the Janelia Research Campus (Mouse Lab). In one line: a universal mask predictor that handles essentially any microscopy image — fluorescence, H&E tissue, live-cell, electron microscopy — without per-experiment retraining. Classical segmentation (watershed, thresholding) requires re-tuning whenever cell type or stain changes. Cellpose trains a U-Net with vector flow representations so it generalizes across morphologies and modalities without needing prior knowledge. The analogy: 'as GPT handles any text, Cellpose handles any cell image.' For life-science researchers, the value is (1) zero-shot use on most images, (2) coverage across fluorescent, H&E, live-cell, and EM modalities, (3) a GUI that lets non-coders click their way to masks. Cellpose 2.0+ adds human-in-the-loop training: label 100-200 images and bootstrap a domain-specific model fast. On data security: fully local execution means clinical or unpublished experimental images never leave the lab. On cost: zero license fees, CPU-only inference is possible (slow), and Cellpose 3.0 ships fast nuclei models plus SAM integration, cementing its place as the best free cell-segmentation tool.

Deep learning-based universal cell segmentation — automatic mask generation for fluorescence and brightfield images.

Automatic nuclei/cell segmentation in fluorescence microscopy: Run the 'cyto3' model on DAPI + membrane-marker 2-channel images to extract thousands of cell masks in seconds (1-2s per 1024x1024 frame on GPU). Output NumPy masks enable area/intensity/adjacency analysis.