BibTex format
@article{Yang:2020:10.1016/j.future.2020.02.005,
author = {Yang, G and Chen, J and Gao, Z and Li, S and Ni, H and Angelini, E and Wong, T and Mohiaddin, R and Nyktari, E and Wage, R and Xu, L and Zhang, Y and Du, X and Zhang, H and Firmin, D and Keegan, J},
doi = {10.1016/j.future.2020.02.005},
journal = {Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications},
pages = {215--228},
title = {Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention},
url = {http://dx.doi.org/10.1016/j.future.2020.02.005},
volume = {107},
year = {2020}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - 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.
AU - Yang,G
AU - Chen,J
AU - Gao,Z
AU - Li,S
AU - Ni,H
AU - Angelini,E
AU - Wong,T
AU - Mohiaddin,R
AU - Nyktari,E
AU - Wage,R
AU - Xu,L
AU - Zhang,Y
AU - Du,X
AU - Zhang,H
AU - Firmin,D
AU - Keegan,J
DO - 10.1016/j.future.2020.02.005
EP - 228
PY - 2020///
SN - 0167-739X
SP - 215
TI - Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention
T2 - Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications
UR - http://dx.doi.org/10.1016/j.future.2020.02.005
UR - http://hdl.handle.net/10044/1/77390
VL - 107
ER -
