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Cited by 77Medical Image Segmentation TechniquesAdvanced Radiotherapy TechniquesArtificial Intelligence in Healthcare and Education

A review of deep learning-based three-dimensional medical image registration methods

Open access
Xiao, Haonan, Teng, Xinzhi, Liu, Chenyang, Li, Tian, Ren, Ge, Yang, Ruijie, Shen, Dinggang, Cai, Jing·2021·Quantitative Imaging in Medicine and Surgery
DOI 10.21037/qims-21-175PMC PMC8611468
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Abstract

Medical image registration is a vital component of many medical procedures, such as image-guided radiotherapy (IGRT), as it allows for more accurate dose-delivery and better management of side effects. Recently, the successful implementation of deep learning (DL) in various fields has prompted many research groups to apply DL to three-dimensional (3D) medical image registration. Several of these efforts have led to promising results. This review summarized the progress made in DL-based 3D image registration over the past 5 years and identify existing challenges and potential avenues for further research. The collected studies were statistically analyzed based on the region of interest (ROI), image modality, supervision method, and registration evaluation metrics. The studies were classified into three categories: deep iterative registration, supervised registration, and unsupervised registration. The studies are thoroughly reviewed and their unique contributions are highlighted. A summary is presented following a review of each category of study, discussing its advantages, challenges, and trends. Finally, the common challenges for all categories are discussed, and potential future research topics are identified.

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  • Previous reviews about deep learning-based medical image registration literature have been published.

    “Though previous reviews about deep learning-based medical image registration literature have been published (19–22), there are still some deficiencies.”

    From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [22]

  • Previous reviews lack a summary of public datasets for benchmarking registration algorithms.

    “On one hand, these reviews lack a summary of the public dataset for benchmarking registration algorithms.”

    From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [22]

    The Review Article section says few earlier reviews focused on 3D medical image registration, but does not say they lacked public-dataset summaries. The Statistical analysis section explains why public datasets are useful for fair benchmarking and says this review summarizes them in Table 1; neither passage establishes what earlier reviews omitted. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

  • Some details of the selected literature were missing in previous reviews.

    “On the other hand, some details of the selected literature were missing.”

    From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [22]

    The Review Article section says that few earlier reviews focused on 3D medical image registration, but it does not say they omitted details of their selected literature. Searches for missing or omitted details in previous reviews found no passage addressing that claim. No page number is available in our copy. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Xiao, H., Teng, X., Liu, C., Li, T., Ren, G., Yang, R., Shen, D., & Cai, J.
Affiliations
  • Hong Kong Polytechnic University (HK)
  • Peking University Third Hospital (CN)
  • Korea University (KR)
Corresponding author
Cai, Jing
ORCID
Venue
Quantitative Imaging in Medicine and Surgery
Publisher
AME Publishing Company
Published
2021-12
Volume
11
Issue
12
Pages
4895-4916
ISSN
  • 2223-4292 (ISSN-L)
  • 2223-4306
DOI
10.21037/qims-21-175
PMC
PMC8611468
OpenAlex
W3186742534
Cited by
77
References
14
Citations by year
2026: 9 · 2025: 18 · 2024: 13 · 2023: 25 · 2022: 10 · 2021: 2
FWCI
0.78
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Quantitative Imaging in Medicine and Surgery (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion, cc-by-nc-nd)
Funders
  • University Grants Committee
  • Food and Health Bureau
  • Health and Medical Research Fund (06173276, HMRF 06173276)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
medical image registration, three-dimensional image registration, deep learning, image-guided radiotherapy, unsupervised registration, imaging modalities, region of interest

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