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Total Segmentator

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total-segmentator

Total Segmentator is an Agent Skill for documents published in the mkurman/zorai repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Tool for robust segmentation of 104+ anatomical structures in CT images. Uses nnUNet-based models for whole-body, organ, and bone segmentation. One-line CLI for comprehensive body-part segmentation.

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How to use this skill

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  2. Copy and paste the prompt below.
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Inspect the Agent Skill at https://github.com/mkurman/zorai/blob/HEAD/skills/scientific-skills/total-segmentator/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/total-segmentator, but wait for my approval before writing files or running scripts.

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Overview

TotalSegmentator segments 104+ anatomical structures in CT images using nnUNet-based models. Run full-body, organ, or bone segmentation with a single CLI command.

Installation

uv pip install TotalSegmentator

CLI Usage

# Full body segmentation (all 104 structures)
TotalSegmentator -i input_ct.nii.gz -o output_seg.nii.gz

# Organ-only segmentation (liver, kidneys, spleen, etc.)
TotalSegmentator -i input_ct.nii.gz -o organ_seg.nii.gz -ta organ

# Appendicular bones
TotalSegmentator -i input_ct.nii.gz -o bone_seg.nii.gz -ta appendicular_bones

Python API

from totalsegmentator.python_api import totalsegmentator

segmentation = totalsegmentator("input_ct.nii.gz", "output_seg.nii.gz")

Workflow

  1. Obtain CT scan in NIfTI format
  2. Run TotalSegmentator -i input.nii.gz -o output.nii.gz
  3. Task types: total (104 structures), organ, vertebra, ribs, appendicular_bones
  4. Output is a multi-label segmentation mask
  5. Extract volumes per label for quantitative analysis