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

Guang Yang,Jun Chen,Zhifan Gao,Shuo Li,Hao Ni,Elsa Angelini,Tom Wong,Raad Mohiaddin,Eva Nyktari,Ricardo Wage,Lei Xu,Yanping Zhang,Xiuquan Du,Heye Zhang,David Firmin,Jennifer Keegan

doi:10.1016/j.future.2020.02.005

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.

Highlights

Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial (LA) scars 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 [1,2,3]
To address the above problems, we propose a fully automated multiview two-task (MVTT) recursive attention model to segment LA anatomy and LA scars from LGE CMR simultaneously
In order to test the efficacy of our proposed MVTT recursive attention model, we retrospectively studied 190 3D LGE CMR scans, and divided these data into a training/ten-fold cross-validation dataset (170 3D scans) and an independent testing dataset (20 3D scans with randomly selected 10 pre-ablation and 10 postablation cases)

Summary

Introduction

Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial (LA) scars 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 [1,2,3]. In the AF patient population, prolonged scanning time, irregular breathing pattern and heart rate variability during the scan can result in poor image quality that can further complicate both segmentation tasks Because of these issues, previous studies have segmented the LA anatomy from an additional bright-blood data acquisition, and have registered the segmented LA anatomy to the LGE CMR data for visualisation and delineation of the LA scars [7,8,9]. Previous studies have segmented the LA anatomy from an additional bright-blood data acquisition, and have registered the segmented LA anatomy to the LGE CMR data for visualisation and delineation of the LA scars [7,8,9] This approach is complicated by motion (bulk, respiratory or cardiac) between the two acquisitions and subsequent registration errors. It is inadequate to achieve accurate and efficient estimation for the LA scars because the LA anatomy and LA scars segmentations are separately handled, and no feedback connection exists between them during the algorithm training

Methods

Results

Discussion

Conclusion