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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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Imaging Studies III: Computed Tomography01:27

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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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Related Experiment Video

Updated: Dec 13, 2025

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
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Evaluating body composition by combining quantitative spectral detector computed tomography and deep learning-based

David Zopfs1, Khaled Bousabarah2, Simon Lennartz3

  • 1University Cologne, Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, Kerpener Straße 62, 50937, Cologne, Germany.

European Journal of Radiology
|July 28, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces automated body composition analysis software using deep convolutional neural networks (DCNN) and dual-energy CT scans. The toolkit accurately differentiates fat and muscle, offering a precise method for body composition assessment.

Keywords:
Body compositionIntra-abdominal fatMachine learningSarcopeniaTomographyX-ray computed

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Area of Science:

  • Medical Imaging
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Accurate body composition analysis is crucial for clinical decision-making.
  • Traditional methods for body composition assessment can be invasive or imprecise.
  • Computed tomography (CT) offers detailed anatomical information but requires robust analysis tools.

Purpose of the Study:

  • To develop and evaluate a software toolkit for fully automated body composition analysis.
  • To leverage dual-energy CT quantitative data and deep convolutional neural networks (DCNN) for enhanced accuracy.
  • To integrate simple detection and segmentation tasks using DCNN for efficient analysis.

Main Methods:

  • Training and validation of DCNN using public and private datasets.
  • A combined approach of DCNN and quantitative thresholding for slice classification and voxel segmentation (fat, muscle, subcutaneous fat, visceral fat).
  • Validation through repetitive patient examinations and concurrent bioelectrical impedance analysis (BIA), employing CCC, linear regression, and Bland-Altman analysis.

Main Results:

  • Automated slice extraction accuracy of 98.7%.
  • High DCNN-based segmentation accuracy for subcutaneous fat (Dice similarity coefficient of 0.95).
  • Excellent agreement (CCC: 0.99 for muscle/subcutaneous fat, 0.98 for visceral fat) in repetitive examinations and good agreement (r²: 0.67-0.88) with BIA.

Conclusions:

  • A novel software toolkit enables accurate body composition analysis.
  • The toolkit effectively combines DCNN and threshold-based segmentation from spectral detector CT.
  • This automated approach provides a reliable tool for quantitative body composition assessment.