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データ・研究 · SKILL.md

auxiliary-analysis

auxiliary-analysis

Use TotalSegmentator auxiliary analysis CLIs for contrast phase, CT/MR modality, body statistics, and Evans index.

Independent GitHub snapshot · Automated classification · Original documentation governs

スキルの説明

GitHub

name: auxiliary-analysis description: "Use TotalSegmentator auxiliary analysis CLIs for contrast phase, CT/MR modality, body statistics, and Evans index." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Auxiliary Analysis

Use this sub-skill when the task is not to run a general segmentation, but to run one of TotalSegmentator's secondary analysis commands: contrast phase prediction, CT/MR modality prediction, body-stat prediction, or Evans index calculation.

TotalSegmentator and these helper outputs are research/workflow aids, not standalone clinical decision systems. Preserve input provenance, inspect generated JSON/PNG outputs, and route license, install, and backend setup questions to the appropriate sibling skill.

Start Here

  • Build a safe command without running models: python scripts/build_auxiliary_command.py <phase|modality|body-stats|evans-index> ....
  • Read references/auxiliary-tools.md for command flags, output schemas, dependency notes, and reuse limits.
  • Read references/troubleshooting.md for optional dependency, license, classifier resource, output path, and expensive-rerun fixes.
  • Use totalseg_get_phase for CT contrast phase JSON with pi_time, phase label, confidence probability, min/max, and ensemble stddev.
  • Use totalseg_get_modality for CT/MR classification JSON; add -n only when intensities are normalized and ROI-based prediction is needed.
  • Use totalseg_get_body_stats for weight, size, age, sex, BMI, and BSA JSON from CT/MR images; default CNN inference is usually preferred over XGBoost.
  • Use totalseg_evans_index for Evans index JSON plus a required preview PNG for visual validation.

Route Elsewhere

  • For selectable task/class discovery, registry APIs, or license-required task inventory, use ../capability-discovery/SKILL.md.
  • For ordinary TotalSegmentator -i ... -o ... segmentation runs and python_api.totalsegmentator(...), use ../segmentation-workflows/SKILL.md.
  • For run reports, statistics.json, multilabel masks, probabilities, and mask combination, use ../outputs-and-statistics/SKILL.md.
  • For installation, model weights, offline caches, license setup, device/backend setup, and usage-stat configuration, use ../runtime-configuration/SKILL.md when present in this generated skill.
  • For DICOM input layout, NIfTI/DICOM conversion, DICOM outputs, and crop_to_body, use ../dicom-and-formats/SKILL.md when present in this generated skill.

Safe Operating Rules

  • Prefer the bundled command builder for automation because it prints shell-quoted commands and dependency cautions without importing TotalSegmentator or running inference.
  • Always write JSON outputs to explicit paths; for Evans index, always request and inspect the preview PNG before trusting the numeric result.
  • Treat optional dependencies as workflow-specific: XGBoost for phase/modality/XGBoost body stats, PyTorch+timm+MONAI for body-stats CNN, and antspyx+blosc for Evans index registration/serialization.
  • Do not rerun expensive segmentation blindly. Reuse documented existing statistics for contrast phase when available; body-stats CLI does not expose a safe existing-statistics flag.
  • Keep general segmentation planning and task/ROI selection out of this sub-skill unless the auxiliary tool itself invokes segmentation internally.
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