The multiview two-task recursive attention model accurately segmented left atrium anatomy and scars simultaneously from 3D LGE CMR images, achieving mean Dice scores of 93% and 87%, respectively.
Does a multiview two-task recursive attention deep learning model improve the simultaneous segmentation of left atrial anatomy and scar from 3D LGE CMR in patients with atrial fibrillation?
The proposed MVTT deep learning model accurately and efficiently segments both left atrial anatomy and scar tissue simultaneously from a single 3D LGE CMR scan in patients with atrial fibrillation.
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.
Yang et al. (Tue,) conducted a other in Atrial fibrillation (n=190). Multiview two-task (MVTT) recursive attention model vs. Manual segmentation and other deep learning models (U-Net, V-Net) was evaluated on Dice score for LA scar segmentation (independent testing). The multiview two-task recursive attention model accurately segmented left atrium anatomy and scars simultaneously from 3D LGE CMR images, achieving mean Dice scores of 93% and 87%, respectively.