Automatic Bi-Atrial Segmentation and Biomarker Extraction from Late Gadolinium-Enhanced MRI Using Deep Learning.

ABSTRACT: Atrial fibrillation (AF) is associated with progressive structural remodeling of the atria, including chamber dilation, fibrosis, and variations in atrial wall thickness (AWT). Late gadolinium-enhanced (LGE) magnetic resonance imaging (MRI) has been used to quantify left atrium (LA) fibrosis for guiding adjunctive ablation beyond pulmonary vein isolation, though results have varied. A major limitation is the lack of a robust segmentation method for accurately assessing both atrial anatomy and fibrosis, coupled with the exclusion of the right atrium (RA) in the analysis. This study introduces biAtriaNet, a deep learning pipeline developed to automate segmentation of both LA and RA and to evaluate atrial fibrosis, AWT, and chamber diameter and volume from LGE-MRIs to support targeted AF abla

SUBMITTER: Feng F 

PROVIDER: S-EPMC12904474 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature