The DOI matches its Crossref record; 214 of the 214 references it cites were found in a registry or an index.
Deep learning in medical image registration: a review
Open accessAbstract
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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“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 [20]
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“DL for medical image registration has numerous applications, which were listed by some review papers [320–322].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [320]
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“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 [20]
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“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 [20]
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Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
Cited by
Sorted by citations
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906
Unberath, M., Gao, C., Hu, Y., Judish, M., Taylor, R. H., Armand, M., & Grupp, R. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.716007 · Cited by 102
“Inspiration can be drawn from the applications of 3D/3D and 2D/2D deformable image registration [110, 111, 112], where machine learning techniques have dominated most existing research into registration uncertainty.”
BackgroundA review of deep learning-based deformable medical image registration
Zou, J., Gao, B., Song, Y., & Qin, J. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.1047215 · Cited by 91
Fu, Y., Lei, Y., Wang, T., Patel, P., Jani, A. B., Mao, H., Curran, W. J., Liu, T., & Yang, X. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101845 · Cited by 75
Self-citationHsu, S.-T., Su, Y.-J., Hung, C.-H., Chen, M.-J., Lu, C.-H., & Kuo, C.-E. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-02047-6 · Cited by 58
Deep reinforcement learning and its applications in medical imaging and radiation therapy: a survey
Xu, L., Zhu, S., & Wen, N. (2022) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ac9cb3 · Cited by 35
Metadata
- Authors
- Fu, Y., Lei, Y., Wang, T., Curran, W. J., Liu, T., & Yang, X.
- Affiliations
- Emory University (US)
- Corresponding author
- Lei, Yang
- 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
- 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
- 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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