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Deep learning for cardiac image segmentation: A review

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Title: Deep learning for cardiac image segmentation: A review
Authors: Chen, C
Qin, C
Qiu, H
Tarroni, G
Duan, J
Bai, W
Rueckert, D
Item Type: Journal Article
Abstract: Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research.
Issue Date: 5-Mar-2020
Date of Acceptance: 17-Feb-2020
URI: http://hdl.handle.net/10044/1/77209
DOI: 10.3389/fcvm.2020.00025
ISSN: 2297-055X
Publisher: Frontiers Media
Start Page: 1
End Page: 33
Journal / Book Title: Frontiers in Cardiovascular Medicine
Volume: 7
Copyright Statement: © 2020 Chen, Qin, Qiu, Tarroni, Duan, Bai and Rueckert. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY)(http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: eess.IV
Notes: Under review
Open Access location: https://www.frontiersin.org/articles/10.3389/fcvm.2020.00025/pdf
Article Number: 25
Online Publication Date: 2020-03-05
Appears in Collections:Computing
Department of Brain Sciences