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Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention

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Title: Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention
Authors: Yang, G
Chen, J
Gao, Z
Li, S
Ni, H
Angelini, E
Wong, T
Mohiaddin, R
Nyktari, E
Wage, R
Xu, L
Zhang, Y
Du, X
Zhang, H
Firmin, D
Keegan, J
Item Type: Journal Article
Abstract: Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment success. This requires a segmentation of the high intensity scar tissue and also a segmentation of the left atrium (LA) anatomy, the latter usually being derived from a separate bright-blood acquisition. Performing both segmentations automatically from a single 3D LGE CMR acquisition would eliminate the need for an additional acquisition and avoid subsequent registration issues. In this paper, we propose a joint segmentation method based on multiview two-task (MVTT) recursive attention model working directly on 3D LGE CMR images to segment the LA (and proximal pulmonary veins) and to delineate the scar on the same dataset. Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (0.27 s to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60–68 2D slices). Compared to conventional unsupervised learning and other state-of-the-art deep learning based methods, the proposed MVTT model achieved excellent results, leading to an automatic generation of a patient-specific anatomical model combined with scar segmentation for patients in AF.
Issue Date: Jun-2020
Date of Acceptance: 1-Feb-2020
URI: http://hdl.handle.net/10044/1/77390
DOI: 10.1016/j.future.2020.02.005
ISSN: 0167-739X
Publisher: Elsevier
Start Page: 215
End Page: 228
Journal / Book Title: Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications
Volume: 107
Copyright Statement: ©2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Sponsor/Funder: British Heart Foundation
British Heart Foundation
National Institutes of Health
Funder's Grant Number: PG/16/78/32402
PG/17/81/33345
NIHR
Keywords: eess.IV
eess.IV
cs.CV
Distributed Computing
0803 Computer Software
0805 Distributed Computing
0806 Information Systems
Publication Status: Published
Online Publication Date: 2020-02-04
Appears in Collections:National Heart and Lung Institute