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

Yang, G; Chen, J; Gao, Z; Li, S; Ni, H; Angelini, E; Wong, T; ... Keegan, J; + view all (2020) Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention. Future Generation Computer Systems , 107 pp. 215-228. 10.1016/j.future.2020.02.005. Green open access

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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.

Type: Article
Title: Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.future.2020.02.005
Publisher version: https://doi.org/10.1016/j.future.2020.02.005
Language: English
Additional information: Copyright © 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/).
Keywords: Late gadolinium enhancement, Medical image segmentation, Deep learning, Attention model, Deep learning interpretation of biomedical data, Atrial fibrillation
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Mathematics
URI: https://discovery.ucl.ac.uk/id/eprint/10091695
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