Deep learning for cardiac image segmentation: A review
File(s)
Published version
Author(s)
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.
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.
Date Issued
2020-03-05
Date Acceptance
2020-02-17
Citation
Frontiers in Cardiovascular Medicine, 2020, 7, pp.1-33
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.
Identifier
http://arxiv.org/abs/1911.03723v1
Subjects
eess.IV
eess.IV
cs.CV
cs.LG
q-bio.QM
Notes
Under review
Article Number
25
Date Publish Online
2020-03-05