Research progress on imaging segmentation and quantification methods for epicardial adipose tissue and its clinical applications

doi:10.3969/j.issn.1006-7795.2025.01.016

Abstract

Abstract: Epicardial adipose tissue (EAT) is a type of fat tissue that is closely adjacent to the coronary arteries and myocardium, and it caused physiological and pathological changes to the body through the secretion of autocrine and paracrine active factors. EAT is regarded as a diagnostic marker and a potential therapeutic target for cardiovascular diseases, and it is of great significance to segment and quantify EAT. This article introduced the evolution of the EAT segmentation and quantification methods from the aspects of traditional imaging, atlas, and artificial intelligence. Furthermore, it reviewed the research progresses on automatically quantified EAT indices in the diagnosis and treatment of cardiovascular diseases.

Key words: epicardial adipose tissue, segmentation and quantification, deep learning, clinical application

CLC Number:

R318
TP18

Qu Junda, Yang Minfu, Li Chunlin, Sun Liwei, Gao He, Zhang Xu. Research progress on imaging segmentation and quantification methods for epicardial adipose tissue and its clinical applications[J]. Journal of Capital Medical University, 2025, 46(1): 99-105.

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URL: https://journal03.magtech.org.cn/Jweb_sdykdxxb/EN/10.3969/j.issn.1006-7795.2025.01.016

https://journal03.magtech.org.cn/Jweb_sdykdxxb/EN/Y2025/V46/I1/99

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