Deep learning-based workflow for automatic extraction of atria and epicardial adipose tissue on cardiac computed tomography in atrial fibrillation.

ABSTRACT:

Background

Preoperative estimation of the volume of the left atrium (LA) and epicardial adipose tissue (EAT) on computed tomography (CT) images is associated with an increased risk of atrial fibrillation (AF) recurrence. We aimed to design a deep learning-based workflow to provide reliable automatic segmentation of the atria, pericardium, and EAT for future applications in the management of AF.

Methods

This study enrolled 157 patients with AF who underwent first-time catheter ablation between January 2015 and December 2017 at Taipei Veterans General Hospital. Three-dimensional (3D) U-Net models of the LA, right atrium (RA), and pericardium were used to develop a pipeline for total, LA-EAT, and RA-EAT automatic segmentation. We defined fat within the pericardium as tissue wit

SUBMITTER: Kuo L 

PROVIDER: S-EPMC12718836 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature