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Automatic quantification of epicardial adipose tissue volume.

Xiaogang Li1, Yu Sun1, Lisheng Xu2

  • 1Department of Radiology, General Hospital of Northern Theater Command, Shenyang, 110016, China.

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Summary

A novel automatic method accurately quantifies epicardial adipose tissue (EAT) volume from coronary computed tomography angiography (CCTA) scans. This tool shows high agreement with expert assessments, aiding cardiovascular disease research.

Keywords:
coronary computed tomography angiography (CCTA)deep neural networkepicardial adipose tissuepericardium segmentation

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Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Epicardial adipose tissue (EAT) volume is linked to cardiovascular diseases.
  • Accurate EAT quantification is crucial for research.
  • Current methods may be time-consuming or subjective.

Purpose of the Study:

  • To develop a fully automatic framework for EAT segmentation and quantification.
  • To assess the accuracy and reliability of the automated method using coronary computed tomography angiography (CCTA) scans.

Main Methods:

  • A dataset of 103 CCTA scans was used.
  • A deep neural network segmented the pericardium across multiple slices.
  • A deformable model refined segmentation, followed by thresholding for EAT extraction.

Main Results:

  • Pericardial segmentation achieved Dice indices of ~97% compared to experts.
  • EAT segmentation showed Dice indices of ~93% compared to experts.
  • EAT volume correlation coefficients with expert assessments were 0.99-1.00.

Conclusions:

  • The developed method provides fully automatic EAT segmentation and quantification from CCTA.
  • The automated approach demonstrates high accuracy and reliability, comparable to expert evaluations.
  • The method is available with a graphical user interface for wider accessibility.