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name: segmentation-workflows description: "Build reproducible TotalSegmentator CLI and Python API segmentation workflows for CT and MR inputs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Segmentation Workflows

Use this sub-skill when an agent needs to run or script TotalSegmentator segmentation itself: choosing the task, input/output, device, speed mode, ROI subset, report, and statistics options.

TotalSegmentator is not a medical device and is not intended for clinical usage. Treat outputs as research or workflow artifacts unless the surrounding product has its own approved clinical validation.

Start Here

1. Select the task and ROI class names before building the command. If the task or class list is unknown, route to ../capability-discovery/SKILL.md and query totalseg_info instead of hard-coding names.
2. Build a reproducible command with scripts/build_segmentation_command.py or the flag patterns in references/workflow-recipes.md.
3. Prefer --report <path.json> for automation so downstream steps can inspect the resolved task, device, classes, and output files without scraping stdout.
4. Add --quiet for non-interactive pipelines and --debug when diagnosing argument/runtime errors.
5. For CPU-only runs, prefer --fast or --roi_subset to reduce runtime and memory.

Local References

• references/cli-and-api.md explains the TotalSegmentator CLI flags and python_api.totalsegmentator(...) call shape.
• references/workflow-recipes.md gives ready-to-adapt CLI and Python workflow recipes.
• references/troubleshooting.md maps common segmentation failures to fixes or sibling routes.
• scripts/build_segmentation_command.py safely prints shell-quoted commands; it never imports TotalSegmentator or runs model inference.

Route Elsewhere

• Use ../outputs-and-statistics/SKILL.md for detailed run-report parsing, statistics schemas, multilabel/probability post-processing, and mask combination.
• Use ../runtime-configuration/SKILL.md for installation, model downloads, offline weights, license setup, usage-stat configuration, and backend/runtime setup.
• Use ../dicom-and-formats/SKILL.md for DICOM input layout, DICOM SEG/RTSTRUCT dependencies, crop_to_body, and format conversion decisions.
• Use ../auxiliary-analysis/SKILL.md for phase, modality, body-stats, and Evans-index helper CLIs.
• Use ../advanced-training/SKILL.md for training, evaluation, dataset conversion, or other research workflows beyond ordinary inference.

Safe Defaults

• Default CT workflow: TotalSegmentator -i ct.nii.gz -o seg/ -ta total --report seg/run_report.json --quiet.
• Default MR workflow: TotalSegmentator -i mr.nii.gz -o seg_mr/ -ta total_mr --report seg_mr/run_report.json --quiet.
• CPU-constrained workflow: add --device cpu --fast and narrow with --roi_subset <class...> when clinically/research appropriate.
• Python API workflow: call totalsegmentator(input, output, task=..., device=..., report=...); pass an output path whenever saving masks, reports, statistics, or radiomics is required.