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Related Experiment Video

Updated: Nov 14, 2025

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
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Artificial intelligence-aided CT segmentation for body composition analysis: a validation study.

Pablo Borrelli1, Reza Kaboteh2, Olof Enqvist3,4

  • 1Region Västra Götaland, Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden. pablo.borrelli@vgregion.se.

European Radiology Experimental
|March 11, 2021
PubMed
Summary
This summary is machine-generated.

An artificial intelligence (AI) tool accurately quantifies three-dimensional subcutaneous adipose tissue (SAT) and muscle volumes from CT scans. This AI method offers a more relevant body composition analysis than traditional single-slice manual segmentation for oncological patients.

Keywords:
Body compositionMusclesNeural networks (computer)Subcutaneous fatTomography (x-raycomputed)

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Body composition significantly impacts survival in cancer patients but is not routinely assessed.
  • Manual segmentation of subcutaneous adipose tissue (SAT) and muscle from CT scans is time-consuming and limited to single slices.
  • Automated quantification of 3D body composition from CT images is needed.

Purpose of the Study:

  • To develop an artificial intelligence (AI)-based method for automated quantification of three-dimensional SAT and muscle volumes from CT images.
  • To assess the accuracy and reproducibility of the AI method compared to manual segmentation.

Main Methods:

  • Convolutional neural networks were trained using manual segmentations on CT images from 50 cancer patients.
  • The AI method was validated on a separate test group of 74 cancer patients.
  • Accuracy was measured by comparing AI-derived volumes with manual segmentations.

Main Results:

  • The AI method achieved high accuracy: 0.96 for SAT and 0.94 for muscle.
  • AI-based volume differences were significantly lower than single-slice area differences (1.8% vs. 5.0% for SAT, 1.9% vs. 3.9% for muscle).
  • The AI tool demonstrated high accuracy and reproducibility for body composition analysis.

Conclusions:

  • The AI-based tool accurately quantifies three-dimensional SAT and muscle volumes from CT images.
  • This automated approach provides a more relevant body composition analysis compared to manual single-slice methods.
  • The AI tool has the potential to improve body composition assessment in oncological patients.