Chen, X orcid.org/0000-0003-4203-4578, Diaz-Pinto, A orcid.org/0000-0002-4865-8296, Ravikumar, N et al. (1 more author) (2021) Deep learning in medical image registration. Progress in Biomedical Engineering, 3. 012003. ISSN 2516-1091
Abstract
Image registration is a fundamental task in multiple medical image analysis applications. With the advent of deep learning, there have been significant advances in algorithmic performance for various computer vision tasks in recent years, including medical image registration. The last couple of years have seen a dramatic increase in the development of deep learning-based medical image registration algorithms. Consequently, a comprehensive review of the current state-of-the-art algorithms in the field is timely, and necessary. This review is aimed at understanding the clinical applications and challenges that drove this innovation, analysing the functionality and limitations of existing approaches, and at providing insights to open challenges and as yet unmet clinical needs that could shape future research directions. To this end, the main contributions of this paper are: (a) discussion of all deep learning-based medical image registration papers published since 2013 with significant methodological and/or functional contributions to the field; (b) analysis of the development and evolution of deep learning-based image registration methods, summarising the current trends and challenges in the domain; and (c) overview of unmet clinical needs and potential directions for future research in deep learning-based medical image registration.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Author(s). Published by IOP Publishing Ltd. Original Content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
Keywords: | deep learning, medical image registration, review |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 Apr 2021 07:58 |
Last Modified: | 22 Apr 2021 07:58 |
Status: | Published |
Publisher: | IOP Publishing |
Identification Number: | 10.1088/2516-1091/abd37c |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:173243 |