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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Bibliografiska uppgifter

Huvudupphov:	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
Materialtyp:	Artigo
Språk:	Inglês
Utgiven:	Nature Portfolio 2024-10-01
Serie:	Scientific Reports
Ämnen:	Deep learning Epicardial adipose tissue Coronary computed tomography angiography (CCTA) Segmentation Post-processing
Länkar:	https://doi.org/10.1038/s41598-024-75659-9
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