MouseLand/cellpose

Health v2 · maintenance only

Adoption not part of the score

2331 stars · 644 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Cellpose is a generalist deep learning algorithm for cellular and nucleus segmentation in microscopy images, with human-in-the-loop capabilities and fine-tuning support. It works across diverse imaging conditions and object sizes, including 3D data.

Use cases

• segment cells in microscopy images
• segment nuclei automatically
• fine-tune a segmentation model on my own cell data
• run cell segmentation in 3D
• annotate cells with human-in-the-loop correction
• segment cells in noisy or blurred images

When to choose

• you need generalist cell/nucleus segmentation without training from scratch
• your microscopy data has noise, blur, or varying channel orders
• you want human-in-the-loop correction of segmentations
• you need 3D segmentation support

When to avoid

• you need general object segmentation outside biological imagery
• you need a lightweight non-deep-learning image analysis tool
• you lack GPU resources for fast inference or fine-tuning

Facets

library · maturity active

computer-vision image-processing machine-learning deep-learning bioinformatics computer-vision image-processing machine-learning python cross-platform cell-segmentation microscopy cell-biology human-in-the-loop sam nucleus-segmentation gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="MouseLand/cellpose")