An enhanced deep learning method for the quantification of epicardial adipose tissue

Abstract Epicardial adipose tissue (EAT) significantly contributes to the progression of cardiovascular diseases (CVDs). However, manually quantifying EAT volume is labor-intensive and susceptible to human error. Although there have been some deep learning-based methods for automatic quantification...

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Bibliographic Details

Published in:	Scientific Reports
Main Authors:	Ke-Xin Tang, Xiao-Bo Liao, Ling-Qing Yuan, Sha-Qi He, Min Wang, Xi-Long Mei, Zhi-Ang Zhou, Qin Fu, Xiao Lin, Jun Liu
Format:	Article
Language:	English
Published:	Nature Portfolio 2024-10-01
Subjects:	Deep learning Epicardial adipose tissue Coronary computed tomography angiography (CCTA) Segmentation Post-processing
Online Access:	https://doi.org/10.1038/s41598-024-75659-9