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LA-Net: A multi-task deep network for the segmentation of the left atrium

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Title: LA-Net: A multi-task deep network for the segmentation of the left atrium
Authors: Bharath, A
Uslu, F
Varela Anjari, M
Boniface, G
Mahenthran, T
Chubb, H
Item Type: Journal Article
Abstract: Although atrial fibrillation (AF) is the most common sustained atrial arrhythmia, treatment success for this condition remains suboptimal. Information from magnetic resonance imaging (MRI) has the potential to improve treatment efficacy, but there are currently few automatic tools for the segmentation of the atria in MR images. In the study, we propose a LA-Net, a multi-task network optimised to simultaneously generate left atrial segmentation and edge masks from MRI. LA-Net includes cross attention modules (CAMs) and enhanced decoder modules (EDMs) to purposefully select the most meaningful edge information for segmentation and smoothly incorporate it into segmentation masks at multiple-scales. We evaluate the performance of LA-Net on two MR sequences: late gadolinium enhanced (LGE) atrial MRI and atrial short axis balanced steady state free precession (bSSFP) MRI. LA-Net gives Hausdorff distances of 12.43 mm and Dice scores of 0.92 on the LGE (STACOM 2018) dataset and Hausdorff distances of 17.41 mm and Dice scores of 0.90 on the bSSFP (in-house) dataset without any post-processing, surpassing previously proposed segmentation networks, including U-Net and SEGANet. Our method allows automatic extraction of information about the LA from MR images, which can play an important role in the management of AF patients.
Issue Date: Feb-2022
Date of Acceptance: 23-Sep-2021
URI: http://hdl.handle.net/10044/1/92493
DOI: 10.1109/TMI.2021.3117495
ISSN: 0278-0062
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 456
End Page: 464
Journal / Book Title: IEEE Transactions on Medical Imaging
Volume: 41
Issue: 2
Copyright Statement: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Rosetrees Trust
British Heart Foundation
British Heart Foundation
Funder's Grant Number: A1173/ M577
RE/18/4/34215
RE/18/4/34215
Keywords: Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Interdisciplinary Applications
Engineering, Biomedical
Engineering, Electrical & Electronic
Imaging Science & Photographic Technology
Radiology, Nuclear Medicine & Medical Imaging
Computer Science
Engineering
Image segmentation
Decoding
Image edge detection
Cams
Magnetic resonance imaging
Task analysis
Shape
Squeeze-excitation networks
edge detection
U-Net
a trous convolution
image segmentation
cardiac MRI
ABLATION
CATHETER
MRI
Nuclear Medicine & Medical Imaging
08 Information and Computing Sciences
09 Engineering
Publication Status: Published
Online Publication Date: 2021-10-04
Appears in Collections:Bioengineering
National Heart and Lung Institute
Faculty of Medicine
Faculty of Engineering