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Computed Tomography

Computed Tomography

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: Jul 2, 2025

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Published on: March 21, 2021

Enhanced Muscle and Fat Segmentation for CT-Based Body Composition Analysis: A Comparative Study.

Benjamin Hou¹, Tejas Sudharshan Mathai¹, Jianfei Liu¹

¹National Institutes of Health (NIH) Clinical Center, Bethesda MD, USA.

|February 27, 2024

Summary

An Internal tool demonstrated superior accuracy in segmenting muscle and subcutaneous fat compared to TotalSegmentator. This advancement in body composition analysis offers reliable risk assessments for patients using routine abdominal CT scans.

Keywords:

Body Composition CT Fat Muscle Segmentation Subcutaneous Visceral

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Area of Science:

Radiology and Medical Imaging
Biomedical Engineering
Computational Anatomy

Background:

Body composition analysis from abdominal CT scans provides personalized risk assessments for various clinical outcomes.
Accurate segmentation of muscle and fat (subcutaneous and visceral) is crucial for reliable body composition metrics.

Approach:

Evaluated an Internal segmentation tool against the established TotalSegmentator using 900 abdominal CT series from the SAROS dataset.
Utilized Dice scores for muscle and subcutaneous fat segmentation accuracy and Cohen's Kappa for visceral fat agreement.

Key Points:

The Internal tool achieved higher Dice scores for subcutaneous fat (83.8% vs. 80.8%) and muscle (87.6% vs. 83.2%) segmentation.
Near-perfect agreement (Cohen's Kappa = 0.856) was observed for visceral fat segmentation between the tools.
Strong correlations were found for muscle and subcutaneous fat volumes and muscle attenuation with the Internal tool.

Conclusions:

The Internal tool demonstrates improved performance over TotalSegmentator for key body composition measurements.
The high agreement for visceral fat and strong correlations for other metrics indicate the tool's reliability.
This validated tool has the potential to enhance the accuracy of body composition analysis in clinical practice.