MS Lesion Segmentation Challenge Pipeline (Claude Skill)

End-to-end workflow for the Longitudinal MS Lesion Segmentation Challenge dataset, including data validation, multimodal processing of T1w, T2w, FLAIR, and PD, lesion segmentation, and QC integration.

   
Type Claude Skill
Supplier CUHK-AIM-Group (community OSS, MIT)
Availability GA — part of the NeuroClaw neuroimaging skill library
Pricing Free / OSS (MIT)
Capabilities Read/Write — Claude runs the skill’s Python locally (Bash), not as an MCP tool

How to install

  • Claude Code — clone and copy the skill into your skills directory: 
    git clone https://github.com/CUHK-AIM-Group/NeuroClaw
cp -r NeuroClaw/skills/mschallenge-skill ~/.claude/skills/

    Project-scoped alternative: copy into .claude/skills/ instead. NeuroClaw skills assume the collection’s shared helpers (claw-shell, modality tool skills) and the upstream neuroimaging stack (FreeSurfer/FSL/fMRIPrep/etc.) — install those dependencies, or run the bundled installer for the full environment:

    cd NeuroClaw && python installer/setup.py

    which configures the Python env, CUDA/GPU, and the neuroimaging tools.

What it does

Use this skill whenever the user wants an end-to-end workflow for the Longitudinal MS Lesion Segmentation Challenge dataset, including data validation, multimodal processing of T1w, T2w, FLAIR, and PD, lesion segmentation, and QC integration. Triggers include: ‘MS Lesion Challenge’, ‘MS Lesion’, ‘ISBI MS’, ‘longitudinal MS’, ‘multiple sclerosis lesion’, or any request to run the MS lesion segmentation pipeline.

Primary use cases: ‘MS Lesion Challenge’, ‘MS Lesion’, ‘ISBI MS’, ‘longitudinal MS’, ‘multiple sclerosis lesion’, or any request to run the MS lesion segmentation pipeline.

Notes

Distributed as a SKILL.md (plus code examples) in the NeuroClaw skill library — Claude executes it locally via Bash/Python rather than as an MCP server. Upstream license: MIT. The skill directory upstream is skills/mschallenge-skill.

Sources

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