Meeting Banner
Abstract #2669

Automated Segmentation of the Left Atrium from 3D Late Gadolinium Enhancement Imaging using Deep Learning

Suvai Gunasekaran1, Julia Hwang1, Daming Shen1,2, Aggelos Katsaggelos1,3, Mohammed S.M. Elbaz1, Rod Passman4, and Daniel Kim1,2
1Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States, 2Biomedical Engineering, Northwestern University, Evanston, IL, United States, 3Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States, 4Cardiology, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States

Left atrial (LA) late gadolinium enhancement (LGE) imaging is essential for detecting fibrosis in patients with atrial fibrillation. Unfortunately, slow manual segmentation of LA LGE limits its use in the clinic. The purpose of this study was to develop a fully automated segmentation method for LA LGE images with deep learning. We tested two different U-net architectures that used either 2D or 3D image inputs for training. Our results demonstrate that 3D inputs are superior to 2D, and the 3D U-Net is a promising method to explore further for clinical translation of LA LGE fibrosis quantification.

This abstract and the presentation materials are available to members only; a login is required.

Join Here

Abstract