Research progress on imaging segmentation and quantification methods for epicardial adipose tissue and its clinical applications
10.3969/j.issn.1006-7795.2025.01.016
- VernacularTitle:心外膜脂肪影像分割量化方法及其临床应用的研究进展
- Author:
Junda QU
1
;
Minfu YANG
;
Chunlin LI
;
Liwei SUN
;
He GAO
;
Xu ZHANG
Author Information
1. 首都医科大学生物医学工程学院,北京 100069
- Publication Type:Journal Article
- Keywords:
epicardial adipose tissue;
segmentation and quantification;
deep learning;
clinical application
- From:
Journal of Capital Medical University
2025;46(1):99-105
- CountryChina
- Language:Chinese
-
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.
