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name: totalsegmentator description: "Use TotalSegmentator as an installed package for medical-image segmentation, task discovery, runtime configuration, output parsing, DICOM handling, auxiliary analysis, and advanced training boundaries." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

TotalSegmentator

Use this skill when a user asks about TotalSegmentator, the TotalSegmentator CLI, the totalsegmentator Python package, anatomical CT/MR segmentation tasks, model-weight or license setup, segmentation output parsing, DICOM/NIfTI handling, or TotalSegmentator-style nnU-Net retraining.

TotalSegmentator outputs are research/workflow artifacts unless the surrounding product has its own approved clinical validation. Preserve input provenance, verify task/class choices, and prefer machine-readable JSON outputs over scraping stdout.

Quick Start

1. Discover capabilities first when task or class names are uncertain: totalseg_info --json or capability-discovery.
2. Build segmentation commands through segmentation-workflows; add --report <path.json> for automation.
3. Parse run_report.json, statistics.json, multilabel outputs, probabilities, and combined masks through outputs-and-statistics.
4. Use runtime-configuration for install/import checks, CPU/GPU/MPS decisions, model weights, offline setup, licenses, and usage-stat settings.
5. Use dicom-and-formats before long runs when inputs are DICOM folders/zips, DICOM output is requested, or crop-to-body preprocessing is needed.

Route by User Intent

User intent	Read this
List tasks, classes, modalities, license flags, or valid --roi_subset names	capability-discovery
Build or run a CT/MR segmentation CLI/API workflow	segmentation-workflows
Validate reports, statistics, masks, multilabel headers, probabilities, or combined masks	outputs-and-statistics
Fix install/import/backend issues, choose devices, pre-stage weights, configure offline or licensed tasks	runtime-configuration
Validate NIfTI/DICOM inputs, DICOM SEG/RTSTRUCT outputs, modality detection, or crop-to-body preprocessing	dicom-and-formats
Use contrast phase, CT/MR modality, body stats, or Evans index helper CLIs	auxiliary-analysis
Plan retraining, dataset conversion, benchmark evaluation, or model-contribution work	advanced-training

Safe Install and Smoke Check

Install the public package in the user's chosen environment:

pip install TotalSegmentator

Then run safe checks that do not download model weights:

totalseg_info --json
python scripts/check_install.py --task total --class-name liver --json

The root helper verifies package metadata, importability, registry access, selected task/class names, and console-script availability. It does not run segmentation, validate licenses against the network, or download weights.

Common Workflows

• Automation pipeline: discover task/classes with totalseg_info --json, build a TotalSegmentator command with --report and optional --statistics, then parse JSON files through the outputs sub-skill.
• CPU-only run: choose --device cpu, add --fast or a small --roi_subset, and use the segmentation troubleshooting reference for memory/runtime limits.
• Offline deployment: use runtime configuration to set a writable TotalSegmentator home, pre-stage weights, save any required license, and run read-only diagnostics before inference.
• DICOM output: validate the input as DICOM first, install the needed optional DICOM output dependency, and avoid requesting DICOM output from NIfTI input.
• Advanced research: use the training sub-skill only for explicit retraining/evaluation/contribution requests; ordinary segmentation should stay in the segmentation workflow.

Runtime References

• Read references/task-selection.md for task, class, modality, and license selection patterns.
• Read references/troubleshooting.md for cross-cutting install, data, CLI/API, optional dependency, model-weight, and license failures.
• Read references/repo-provenance.md to decide whether this skill is aligned with the source version.
• references/repo-routing-metadata.json contains structured metadata for managed router import.

Boundaries

• Do not hard-code task/class lists from memory; use totalseg_info or totalsegmentator.registry.
• Do not run heavyweight segmentation, model downloads, licensed tasks, or training by default when the user only needs planning or validation.
• Do not expose license numbers, local cache paths, or environment paths in shared reports.
• Do not treat skipped native examples or heavyweight tests as passing; record why they are skipped and use safe JSON/helper checks where possible.