bio-imaging-mass-cytometry-interactive-annotation
Interactive cell type annotation for IMC data. Covers napari-based annotation, marker-guided labeling, training data generation, and annotation validation. Use when manually annotating cell types for training classifiers or validating automated phenotyping results.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-imaging-mass-cytometry-interactive-annotation --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: matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scikit-learn 1.4+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Interactive Annotation **"Manually annotate cell types in my IMC data"** → Interactively label cells using napari visualization with marker overlays for training classifiers or validating automated phenotyping results. - Python: `napari.Viewer()` with label layer for interactive annotation ## Napari-Based Annotation ```python import napari import numpy as np from skimage import io import pandas as pd # Load IMC image stack image_stack = io.imread('imc_image.tiff') # (C, H, W) segmentation_mask = io.imread('cell_segmentation.tiff') # Create napari viewer viewer = napari.Viewer() # Add channels as separate layers for visualization channel_names = ['CD45', 'CD3', 'CD68', 'panCK', 'DNA'] for i, name in enumerate(channel_names): viewer.add_image(image_sta
- Version Compatibility
- Napari-Based Annotation
- Marker-Guided Annotation
- Training Data Generation
- Semi-Automated Annotation
- Annotation Validation
- Napari Plugin Interface
- Batch Annotation Workflow
- Related Skills
What does the bio-imaging-mass-cytometry-interactive-annotation skill do?
Interactive cell type annotation for IMC data. Covers napari-based annotation, marker-guided labeling, training data generation, and annotation validation. Use when manually annotating cell types for training classifiers or validating automated phenotyping results.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-imaging-mass-cytometry-interactive-annotation --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.