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Technical Note: Automatic segmentation of CT images for ventral body composition analysis.

Yabo Fu1, Joseph E Ippolito1, Daniel R Ludwig1

  • 1Washington University School of Medicine, 660 S Euclid Ave, Campus, Box 8131, St Louis, MO, 63110, USA.

Medical Physics
|September 24, 2020
PubMed
Summary

This study presents an automated method to segment body tissues like subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and muscle from CT scans. This technique allows for precise 3D body composition analysis, aiding in disease risk assessment.

Keywords:
body composition analysisconvolutional neural networksubcutaneous fat segmentationvisceral fat segmentation

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Area of Science:

  • Medical Imaging
  • Computational Anatomy
  • Radiology

Background:

  • Body composition is a key factor in various diseases, including diabetes, cancer, and cardiovascular conditions.
  • Accurate segmentation of body tissues is crucial for quantitative analysis and disease risk assessment.

Purpose of the Study:

  • To develop a fully automated procedure for segmenting major body composition compartments: subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and muscle.
  • To incorporate segmentation of the ventral cavity, lungs, and bones to enhance the accuracy of major compartment segmentation.

Main Methods:

  • A convolutional neural network (CNN) was employed for ventral cavity segmentation.
  • An image processing workflow, including hysteresis thresholding and morphological operations, was developed to segment body tissues.
  • Ventral cavity segmentation was performed first to ensure accurate separation of tissues with similar Hounsfield units.

Main Results:

  • The CNN model achieved high Dice scores for ventral cavity segmentation (0.966 ± 0.012).
  • Accurate segmentation was demonstrated for bone, VAT, SAT, muscle, and lung across different CT contrast types, with Dice scores generally above 0.90.
  • The method showed robust performance on both contrast-enhanced and non-contrast CT datasets.

Conclusions:

  • A fully automated body tissue decomposition procedure was successfully developed.
  • The method enables automated quantification of three-dimensional (3D) body composition metrics from CT images.
  • This automated approach facilitates precise analysis of body composition for improved health assessments.