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Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of leftatrial scar in patients with atrial fibrillation (AF) has recently emerged as apromising technique to stratify patients, to guide ablation therapy and topredict treatment success. This requires a segmentation of the high intensityscar tissue and also a segmentation of the left atrium (LA) anatomy, the latterusually being derived from a separate bright-blood acquisition. Performing bothsegmentations automatically from a single 3D LGE CMR acquisition wouldeliminate the need for an additional acquisition and avoid subsequentregistration issues. In this paper, we propose a joint segmentation methodbased on multiview two-task (MVTT) recursive attention model working directlyon 3D LGE CMR images to segment the LA (and proximal pulmonary veins) and todelineate the scar on the same dataset. Using our MVTT recursive attentionmodel, both the LA anatomy and scar can be segmented accurately (mean Dicescore of 93% for the LA anatomy and 87% for the scar segmentations) andefficiently (~0.27 seconds to simultaneously segment the LA anatomy and scarsdirectly from the 3D LGE CMR dataset with 60-68 2D slices). Compared toconventional unsupervised learning and other state-of-the-art deep learningbased methods, the proposed MVTT model achieved excellent results, leading toan automatic generation of a patient-specific anatomical model combined withscar segmentation for patients in AF.

Simultaneous Left Atrium Anatomy and Scar Segmentations via Deep Learning in Multiview Information with Attention