Cited by 661Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and EducationDomain Adaptation and Few-Shot Learning

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Deep learning in medical image registration: a review

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Fu, Yabo, Lei, Yang, Wang, Tonghe, Curran, Walter J, Liu, Tian, Yang, Xiaofeng·2020·Physics in Medicine & Biology

DOI 10.1088/1361-6560/ab843ePMC PMC7759388

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Abstract

This paper presents a review of deep learning (DL)-based medical image registration methods. We summarized the latest developments and applications of DL-based registration methods in the medical field. These methods were classified into seven categories according to their methods, functions and popularity. A detailed review of each category was presented, highlighting important contributions and identifying specific challenges. A short assessment was presented following the detailed review of each category to summarize its achievements and future potential. We provided a comprehensive comparison among DL-based methods for lung and brain registration using benchmark datasets. Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of DL-based medical image registration.

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CT prostate segmentation based on synthetic MRI‐aided deep attention fully convolution network
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Construction of a Statistical Model for Cardiac Motion Analysis Using Nonrigid Image Registration
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See all 661

Authors: Fu, Y., Lei, Y., Wang, T., Curran, W. J., Liu, T., & Yang, X.
Affiliations: Emory University (US)
Corresponding author: Lei, Yang
Yang, Xiaofeng
ORCID: Fu, Yabo
Lei, Yang
Wang, Tonghe
Curran, Walter J
Liu, Tian
Yang, Xiaofeng
Venue: Physics in Medicine & Biology
Publisher: IOP Publishing
Published: 2020-10-07
Volume: 65
Issue: 20
Pages: 20TR01
ISSN: 0031-9155 (ISSN-L)
1361-6560
DOI: 10.1088/1361-6560/ab843e
PMC: PMC7759388
OpenAlex: W3013056581
Cited by: 661
References: 214
Citations by year: 2026: 55 · 2025: 114 · 2024: 131 · 2023: 132 · 2022: 112 · 2021: 93 · 2020: 22
FWCI: 7.33
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: publishingsupport.iopscience.iop.org/iop-standard/v1 (vor)
iopscience.iop.org/info/page/text-and-data-mining (tdm)
Locations: Physics in Medicine and Biology (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: National Cancer Institute of the National Institutes of Health (R01CA215718)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords: medical image registration, deep learning, brain registration, benchmark dataset, research trends
MeSH terms: Deep Learning, Brain, Diagnostic Imaging, Humans, Image Processing, Computer-Assisted

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Deep learning in medical image registration: a review

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