bio-imaging-mass-cytometry-cell-segmentation
Cell segmentation from multiplexed tissue images. Covers deep learning (Cellpose, Mesmer) and classical approaches for nuclear and whole-cell segmentation. Use when extracting single-cell data from IMC or MIBI images after preprocessing.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-imaging-mass-cytometry-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.
## Version Compatibility Reference examples tested with: Cellpose 3.0+, anndata 0.10+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scanpy 1.10+, steinbock 0.16+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Cell Segmentation for IMC **"Segment cells from my IMC images"** → Identify individual cell boundaries in multiplexed imaging data using deep learning (Cellpose) or watershed-based approaches for single-cell extraction. - Python: `cellpose.models.Cellpose()` for deep learning segmentation - CLI: `steinbock segment` for pipeline-based segmentation ## Cellpose Segmentation ```python from cellpose import models, io import numpy as np import tifffile # Load image img = tifffile.imread('processed.tiff') # Extract nuclear channel (e.g., DNA1) nuclear_channel = img[0] # Adjust index based on panel # Initialize Cellpose model model = models.Cellpose(mode
- Version Compatibility
- Cellpose Segmentation
- Whole-Cell Segmentation with Cellpose
- Mesmer (DeepCell)
- steinbock Segmentation
- Extract Single-Cell Data
- Quality Control
- Expand Nuclei to Cells
- Save Results
- Related Skills
Using steinbock with Cellpose steinbock segment cellpose \ Using steinbock with DeepCell steinbock segment deepcell \
What does the bio-imaging-mass-cytometry-cell-segmentation skill do?
Cell segmentation from multiplexed tissue images. Covers deep learning (Cellpose, Mesmer) and classical approaches for nuclear and whole-cell segmentation. Use when extracting single-cell data from IMC or MIBI images after preprocessing.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-imaging-mass-cytometry-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 FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 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.