cellpose-cell-segmentation
DL cell/nucleus segmentation for fluorescence and brightfield microscopy. Pre-trained models (cyto3, nuclei, tissuenet) and a generalist flow-based algorithm segment cells without retraining. Outputs label masks for morphology and tracking. Use scikit-image watershed for rule-based; Cellpose when DL generalization across staining is needed.
npx skills add BioTender-max/awesome-bio-agent-skills --skill cellpose-cell-segmentation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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-s
- Overview
- When to Use
- Prerequisites
- Quick Start
- Workflow
- Step 1: Load and Inspect Images
- Step 2: Segment Cells with a Pre-trained Model
- Step 3: Segment Nuclei from DAPI Channel
- Step 4: Visualize Segmentation Results
- Step 5: Measure Cell Properties from Masks
- Step 6: Batch Segment Multiple Images
- Key Parameters
- Common Recipes
- Recipe 1: Segment Multichannel Image (GFP + DAPI)
Install Cellpose
pip install cellpose
Install with GUI support
pip install cellpose[gui]
Install with GPU (PyTorch CUDA)
pip install cellpose torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
Verify
python -c "from cellpose import models; print('Cellpose ready')"
CLI batch segmentation of all TIFFs in a directory
cellpose \What does the cellpose-cell-segmentation skill do?
DL cell/nucleus segmentation for fluorescence and brightfield microscopy. Pre-trained models (cyto3, nuclei, tissuenet) and a generalist flow-based algorithm segment cells without retraining. Outputs label masks for morphology and tracking. Use scikit-image watershed for rule-based; Cellpose when DL generalization across staining is needed.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill cellpose-cell-segmentation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.