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Cited by 106Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and EducationDomain Adaptation and Few-Shot Learning

Deep Learning in Medical Image Registration

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Chen, Xiang, Diaz-Pinto, Andres, Ravikumar, Nishant, Frangi, Alejandro·2020·Progress in Biomedical Engineering
DOI 10.1088/2516-1091/abd37c
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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.

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  • In 3D/3D and 2D/2D deformable image registration, machine learning techniques have dominated most existing research into registration uncertainty.

    “Inspiration can be drawn from the applications of 3D/3D and 2D/2D deformable image registration (Chen et al., 2020; Fu et al., 2020; Haskins et al., 2020), where machine learning techniques have dominated most existing research into registration uncertainty.”

    From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Chen et al., 2020

    Section 3.2.5 (page unavailable) says registration studies have primarily focused on 2D–2D and 3D–3D images. Section 2.2.2.4 describes a deep-learning framework for quantifying registration uncertainty. Neither passage establishes that machine-learning techniques dominated most existing research specifically into registration uncertainty. (LLM, conf=0.83)

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Cited by

Sorted by citations

Authors
Chen, X., Diaz-Pinto, A., Ravikumar, N., & Frangi, A.
Affiliations
University of Leeds (GB)
Corresponding author
Frangi, Alejandro
ORCID
Venue
Progress in Biomedical Engineering
Publisher
IOP Publishing
Published
2020-12-14
ISSN
2516-1091 (ISSN-L)
DOI
10.1088/2516-1091/abd37c
OpenAlex
W3113028252
Cited by
106
References
13
Citations by year
2026: 8 · 2025: 26 · 2024: 27 · 2023: 25 · 2022: 12 · 2021: 6 · 2020: 2
FWCI
4.38
Type
journal article
Language
en
Open access
Licence
Locations
  • Progress in Biomedical Engineering (publishedVersion, cc-by)
  • White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
  • Lirias (KU Leuven) (submittedVersion, cc-by)
  • Research Explorer (The University of Manchester) (submittedVersion)
Funders
Engineering and Physical Sciences Research Council (CiET1819\19, EP/N026993/1)
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, unmet clinical needs, future research directions

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