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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...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: 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: Artigo
Sprache:Inglês
Veröffentlicht: Nature Portfolio 2024-10-01
Schriftenreihe:Scientific Reports
Schlagworte:
Deep learning
Epicardial adipose tissue
Coronary computed tomography angiography (CCTA)
Segmentation
Post-processing
Online-Zugang:https://doi.org/10.1038/s41598-024-75659-9
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