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Related Concept Videos

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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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.
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Related Experiment Video

Updated: Dec 8, 2025

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
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Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural

Sven Koitka1, Lennard Kroll2, Eugen Malamutmann3

  • 1Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany. sven.koitka@uk-essen.de.

European Radiology
|September 18, 2020
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Summary

This study introduces automated 3D body composition analysis from CT scans, offering reproducible biomarkers for routine clinical use. The method accurately segments abdominal tissues, improving diagnostic capabilities.

Keywords:
AbdomenBody compositionComputer-assisted image analysisDeep learning

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Area of Science:

  • Radiology and Medical Imaging
  • Biomedical Engineering
  • Quantitative Anatomy

Background:

  • Body tissue composition is a crucial biomarker for various diseases and treatments.
  • Current methods for body composition analysis from CT scans are often limited or not fully automated.
  • Standardized, quantitative analysis is needed for routine clinical application.

Purpose of the Study:

  • To develop a fully automated, reproducible, and quantitative 3D volumetry for body tissue composition analysis.
  • To enable the use of body composition biomarkers in routine clinical imaging.
  • To analyze tissue composition across the entire abdomen, not just specific slices.

Main Methods:

  • Utilized a dataset of 50 CT scans (40 for training, 10 for testing).
  • Employed multi-resolution U-Net 3D neural networks for semantic segmentation of abdominal regions.
  • Subclassified adipose tissue and muscle using Hounsfield unit limits.

Main Results:

  • Achieved an average Sørensen Dice score of 0.9553 for semantic region segmentation.
  • Obtained intra-class correlation coefficients above 0.99 for subclassified tissues.
  • Demonstrated stable biomarkers across the whole abdomen.

Conclusions:

  • Fully automated body composition analysis is feasible on routine abdomen CT scans.
  • The developed method provides accurate and reproducible body composition biomarkers.
  • This approach extends beyond traditional L3 slice analysis for comprehensive assessment.