TotalSegmentator: Instructions file for Codex

Using TotalSegmentator from automation and AI coding agents

This file is for scripts, pipelines and AI coding agents (e.g. Codex, Claude Code) that drive TotalSegmentator non-interactively. It documents how to discover the tool's capabilities, invoke it reproducibly and parse its results without scraping human-readable stdout.

For the full feature documentation see README.md.

TL;DR

# 1. Discover what the tool can do (instant, no GPU, no model download):
totalseg_info --json                       # full capability registry as JSON

# 2. Run a segmentation and capture a machine-readable run report:
TotalSegmentator -i ct.nii.gz -o seg/ --report seg/run_report.json

# 3. Read seg/run_report.json to learn versions, device, classes and output files.

Discovering capabilities

Do not hard-code task names or class names from the source. Query them instead — the data is generated from the same registry the CLI validates against, so it never drifts.

• totalseg_info --list-tasks — table of every task (modality CT/MR, whether a license is required, number of classes).
• totalseg_info --classes -ta <task> — the index -> class_name map a task outputs. The class names are exactly what --roi_subset expects.
• totalseg_info --json — full registry as JSON: { "totalsegmentator_version", "tasks": { <task>: { "modality", "license_required", "classes": { "<index>": "<name>" } } } }.
• Add --json to --list-tasks or --classes to scope the JSON to just that view.

totalseg_info imports no heavy dependencies, needs no GPU and downloads no weights, so it is safe to call at planning time to decide which task/classes to request.

The same lists are available on the main command (TotalSegmentator --list-tasks, TotalSegmentator --list-classes [task]), but those load the full runtime; prefer totalseg_info for quick discovery.

Running a segmentation

TotalSegmentator -i <input> -o <output> -ta <task> [options]

• -i accepts a NIfTI file (.nii / .nii.gz), a folder of DICOM slices, or a zip of DICOM slices. -o is a directory of per-class masks, or a single file when --ml (multilabel), dicom_seg or dicom_rtstruct output is used.
• -d cpu|gpu|gpu:N|mps selects the device. Without a GPU the run falls back to CPU (slow); --fast (3mm) or --roi_subset <classes> reduce runtime and memory.
• --save_lowres writes the segmentation at the model spacing (1.5mm by default, 3mm with --fast, 6mm with --fastest) instead of resampling back to the input resolution.
• Some tasks require a license (see license_required in the registry). Set it once with totalseg_set_license -l <key>; free non-commercial licenses: https://backend.totalsegmentator.com/license-academic/