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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Related Experiment Video

Updated: Jun 26, 2025

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
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Enhanced muscle and fat segmentation for CT-based body composition analysis: a comparative study.

Benjamin Hou1, Tejas Sudharshan Mathai2, Jianfei Liu2

  • 1National Institutes of Health (NIH) Clinical Center, Bethesda, MD, USA. benjamin.hou@nih.gov.

International Journal of Computer Assisted Radiology and Surgery
|May 17, 2024
PubMed
Summary
This summary is machine-generated.

An Internal tool for body composition analysis using abdominal CT scans demonstrated superior accuracy in measuring subcutaneous fat and muscle compared to the TotalSegmentator tool. High agreement was also found for visceral fat segmentation, indicating improved clinical risk assessment potential.

Keywords:
Body compositionCTFatMuscleSegmentationSubcutaneousVisceral

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Area of Science:

  • Radiology and Medical Imaging
  • Biomedical Engineering
  • Quantitative Imaging

Background:

  • Routine abdominal CT scans offer valuable data for body composition analysis, including muscle and fat volumes and attenuation.
  • These metrics are linked to significant clinical outcomes such as cardiovascular events, fractures, and mortality.
  • Accurate segmentation of these tissues is crucial for reliable risk stratification.

Purpose of the Study:

  • To evaluate the reliability of an Internal segmentation tool for muscle, subcutaneous fat, and visceral fat.
  • To compare the performance of the Internal tool against the established public TotalSegmentator tool.
  • To assess the potential of the Internal tool for enhancing body composition analysis.

Main Methods:

  • The study utilized 900 CT series from the SAROS dataset.
  • Segmentation accuracy for subcutaneous fat and muscle was assessed using the Dice score.
  • Segmentation agreement for visceral fat was evaluated using Cohen's Kappa due to the absence of ground truth data.

Main Results:

  • The Internal tool showed improved Dice scores for subcutaneous fat (83.8% vs. 80.8%) and muscle (87.6% vs. 83.2%), with statistically significant differences (p < 0.01).
  • Cohen's Kappa score of 0.856 indicated near-perfect agreement for visceral fat segmentation between the tools.
  • Strong correlations were observed for muscle volume (R²=0.99), muscle attenuation (R²=0.93), and subcutaneous fat volume (R²=0.99).

Conclusions:

  • The Internal tool demonstrated superior performance over TotalSegmentator in segmenting subcutaneous fat and muscle.
  • High agreement for visceral fat segmentation suggests the Internal tool's reliability.
  • These findings highlight the Internal tool's potential to advance the accuracy of body composition analysis from CT scans.