Multiview sequential learning and dilated residual learning for a fully automatic delineation of the left atrium and pulmonary veins from late gadolinium-enhanced cardiac MRI images

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Title: Multiview sequential learning and dilated residual learning for a fully automatic delineation of the left atrium and pulmonary veins from late gadolinium-enhanced cardiac MRI images
Authors: Yang, G
Chen, J
Gao, Z
Zhang, H
Ni, H
Angelini, E
Mohiaddin, R
Wong, T
Keegan, J
Firmin, D
Item Type: Conference Paper
Abstract: Accurate delineation of heart substructures is a prerequisite for abnormality detection, for making quantitative and functional measurements, and for computer-aided diagnosis and treatment planning. Late Gadolinium-Enhanced Cardiac MRI (LGE-CMRI) is an emerging imaging technology for myocardial infarction or scar detection based on the differences in the volume of residual gadolinium distribution between scar and healthy tissues. While LGE-CMRI is a well-established non-invasive tool for detecting myocardial scar tissues in the ventricles, its application to left atrium (LA) imaging is more challenging due to its very thin wall of the LA and poor quality images, which may be produced because of motion artefacts and low signal-to-noise ratio. As the LGE-CMRI scan is designed to highlight scar tissues by altering the gadolinium kinetics, the anatomy among different heart substructures has less distinguishable boundaries. An accurate, robust and reproducible method for LA segmentation is highly in demand because it can not only provide valuable information of the heart function but also be helpful for the further delineation of scar tissue and measuring the scar percentage. In this study, we proposed a novel deep learning framework working on LGE-CMRI images directly by combining sequential learning and dilated residual learning to delineate LA and pulmonary veins fully automatically. The achieved results showed accurate segmentation results compared to the state-of-the-art methods. The proposed framework leads to an automatic generation of a patient-specific model that can potentially enable an objective atrial scarring assessment for the atrial fibrillation patients.
Issue Date: 29-Oct-2018
Date of Acceptance: 18-Jul-2018
URI: http://hdl.handle.net/10044/1/66720
DOI: https://dx.doi.org/10.1109/EMBC.2018.8512550
ISBN: 9781538636466
ISSN: 1557-170X
Publisher: IEEE
Start Page: 1123
End Page: 1127
Journal / Book Title: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Copyright Statement: © 2018 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: British Heart Foundation
British Heart Foundation
Funder's Grant Number: PG/16/78/32402
PG/17/81/33345
Conference Name: 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Publication Status: Published
Start Date: 2018-07-18
Finish Date: 2018-07-21
Conference Place: Honolulu, HI, USA
Online Publication Date: 2018-10-29
Appears in Collections:Division of Surgery
National Heart and Lung Institute
Faculty of Medicine



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