ms-lesion-segmentation

name	ms-lesion-segmentation
description	Workflow for multiple-sclerosis (MS) white-matter lesion segmentation and lesion-load quantification from a T1w + FLAIR pair using LST-AI
license	Apache-2.0
compatibility	Requires the medmcp-neuro-ms MCP server (console script medmcp-neuro-ms).

MS lesion segmentation & lesion-load workflow

segment_ms_lesions runs LST-AI — a deep-learning UNet3D ensemble (native PyTorch) — to segment MS white-matter lesions and report total lesion load (volume mm³), lesion count, and a per-region breakdown. It does its own registration to MNI (picsl-greedy) and skull stripping (HD-BET), so you only provide the raw scans.

When to use

• The user wants MS lesion segmentation, a lesion mask, or lesion load / lesion volume / lesion count from a brain MRI.
• The user has both a T1w and a FLAIR of the same session.

Required inputs

LST-AI needs both a T1w and a FLAIR, co-registered to the same session. If only one contrast is available, tell the user LST-AI cannot run (it is a T1+FLAIR method) — do not substitute another modality.

Steps

1. Identify the T1w and the FLAIR. Match by contrast, not by guessing — the filename usually contains T1w / FLAIR. If you cannot tell which is which, ask.
2. Do not skull-strip or register first. LST-AI handles both internally. Only pass already_stripped=True if the user states both images are already skull-stripped.
3. Run segment_ms_lesions with device="auto" (GPU when present, else CPU — tell the user if it resolves to CPU, which is slower). Keep annotate=True (default) unless the user only wants the binary mask.
4. Report lesion load. From the result: total_lesion_volume_mm3, lesion_count, and the lesion mask path. If region_volumes_mm3 is present, give the per-region breakdown. Follow the result's _render rules.

Region annotation

With annotate=True, lesions are labelled into four McDonald-criteria regions — Periventricular, Juxtacortical, Subcortical, Infratentorial — written to the annotated map and *_annotated_lesion_stats.csv. Call list_ms_lesion_regions() for the exact region names.

Gotchas

• Research only. LST-AI is not validated/approved for clinical use. If the user describes a clinical decision context, flag this clearly before proceeding.
• Output is a binary mask in FLAIR space (*_seg-lst.nii.gz) — offer to overlay it on the FLAIR in the viewer. The annotated map (*_desc-annotated_seg-lst.nii.gz) uses labels 1–4 for the four regions.
• Lesion load comparisons over time need consistent acquisition; raw mm³ differences between scans can reflect protocol/scanner changes, not true disease change — caveat any longitudinal comparison.
• Runtime — minutes on GPU; substantially longer on CPU. The result reports which device was used.
• Errors: report and stop; do not retry with modified inputs without asking the user.