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MHub Segmentation Skill

A Claude skill for discovering MHub medical imaging AI models, looking up anatomical segment codes, and generating workflow configurations for running models on NIfTI files.

Features

  • Model Discovery: Find models by modality (CT, MR, PET), anatomy (liver, lung, heart), or capability
  • Segment Lookup: Get SNOMED CT codes, display colors, and metadata for 155 anatomical structures
  • Workflow Generation: Create custom YAML configs to run MHub models on NIfTI/NRRD files
  • DCMQI Config Generation: Generate metadata JSON for DICOM-SEG conversion
  • Offline-First: Works without network access using bundled cached data

Installation

For Claude.ai

  1. Download or clone this skill folder

  2. Create a ZIP file of the skill:

    cd mhub-segmentation
zip -r ../mhub-segmentation.zip .

  3. In Claude.ai:

    • Go to Settings → Features → Skills
    • Click Add Skill
    • Upload the mhub-segmentation.zip file
    • Enable the skill

  4. The skill will automatically activate when you ask about:

    • MHub models or medical image segmentation
    • Running AI models on NIfTI files
    • SNOMED codes for anatomical structures
    • DICOM-SEG conversion

For Claude Code

  1. Clone or copy the skill to your Claude Code skills directory:

    # Option 1: User skills (available in all projects)
cp -r mhub-segmentation ~/.claude/skills/

# Option 2: Project skills (available in current project)
cp -r mhub-segmentation .claude/skills/

  2. The skill is now available. Claude Code will automatically use it when relevant, or you can invoke it directly:

    /mhub-segmentation

  3. To refresh the model cache (requires network):

    python ~/.claude/skills/mhub-segmentation/scripts/mhub_helper.py refresh

Usage Examples

Find Models

Ask Claude:

  • "What MHub models can segment the liver?"
  • "Show me CT segmentation models"
  • "What models work with MRI data?"

Or use the helper script directly:

python scripts/mhub_helper.py find liver kidney
python scripts/mhub_helper.py models --modality CT
python scripts/mhub_helper.py model totalsegmentator

Run Models on NIfTI Files

Ask Claude:

  • "How do I run TotalSegmentator on my NIfTI files?"
  • "Generate a workflow config for lungmask with BIDS data"
  • "Help me process my CT scans with MHub"

Or generate configs directly:

python scripts/mhub_helper.py config totalsegmentator --pattern flat --output custom.yml

docker run --rm --gpus all \
  -v /path/to/nifti:/app/data/input_data:ro \
  -v /path/to/output:/app/data/output_data \
  -v ./custom.yml:/app/config/custom.yml:ro \
  mhubai/totalsegmentator:latest \
  --config /app/config/custom.yml

Look Up Segment Codes

Ask Claude:

  • "What's the SNOMED code for left kidney?"
  • "Show me segment info for LIVER"
  • "What segments does TotalSegmentator output?"

Or use the script:

python scripts/mhub_helper.py segment LEFT_KIDNEY
python scripts/mhub_helper.py segments --search heart

Skill Contents

mhub-segmentation/
├── SKILL.md                 # Main skill instructions
├── README.md                # This file
├── data/
│   ├── models_cache.json    # Raw MHub API response
│   ├── models_summary.json  # Processed model index
│   └── segdb_cache.json     # SegDB segments with SNOMED codes
├── references/
│   ├── nifti-workflows.md   # Complete NIfTI workflow guide
│   ├── filestructure-patterns.md  # FileStructureImporter syntax
│   └── dataorganizer-patterns.md  # Output organization options
├── scripts/
│   └── mhub_helper.py       # CLI tool for queries and config generation
└── assets/
    └── workflow-templates/
        ├── nifti_generic.yml        # Generic NIfTI template
        ├── bids_template.yml        # BIDS-compatible template
        ├── clinical_trial_template.yml  # Multi-site template
        └── defaults/                # Original configs for 30 models

Cached Data

The skill includes cached data for offline operation:

Data Count Source Cache Date
MHub Models 30 mhub.ai API 2025-01-29
SegDB Segments 155 segdb Python package 2025-01-29
Default Configs 30 MHub GitHub repository 2025-01-29

To update the cache (requires network access):

python scripts/mhub_helper.py refresh

Supported Models

The skill includes data for all 30 MHub models:

CT Segmentation: totalsegmentator (104 structures), platipy (cardiac), lungmask, casust, nnunet_liver, nnunet_pancreas, bamf_nnunet_ct_kidney, gc_lunglobes, nnunet_segthor, gc_autopet_fpr, gc_nnunet_pancreas, msk_smit_lung_gtv

MR Segmentation: mrsegmentator, bamf_nnunet_mr_prostate, bamf_nnunet_mr_liver, nnunet_prostate_zonal_task05, nnunet_prostate_task24, monai_prostate158, gc_spider_baseline

PET/CT: bamf_pet_ct_lung_tumor, bamf_pet_ct_breast_tumor

Prediction: gc_picai_baseline, gc_grt123_lung_cancer, gc_stoic_baseline, fmcib_radiomics, pyradiomics, gc_node21_baseline, gc_wsi_bgseg, gc_tiger_lb2

Requirements

  • Offline use: Python 3.8+ (no additional packages needed)
  • Cache refresh: requests package
  • SegDB Python API: segdb package
  • Running models: Docker with NVIDIA GPU support

License

This skill is provided under the MIT License.

MHub models have individual licenses (typically Apache 2.0 for code, CC BY-NC 4.0 for weights). Check each model's license before use.

Resources