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
Items 201–214 of 214
Motion Tracking and Strain Map Computation for Quasi-Static Magnetic Resonance Elastography
Fu, Y., Chui, C., Teo, C. L., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23623-5_54 · Cited by 15
Self-citationDilated FCN for Multi-Agent 2D/3D Medical Image RegistrationPreprint
Miao, S., Piat, S., Fischer, P., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.01651 · Cited by 15
Weakly Supervised Learning of Metric Aggregations for Deformable Image Registration
Ferrante, E., Dokania, P. K., Silva, R. M., & Nikos Paragios, A. (2019) · Conicet · Cited by 14
An Artificial Agent for Robust Image RegistrationPreprint
Liao, R., Miao, S., de Tournemire, P., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.10336 · Cited by 13
Mahapatra, D., & Ge, Z. (2019) · arXiv (Cornell University) · Cited by 11
Voxel-Based Dose Reconstruction for Total Body Irradiation With Helical TomoTherapy
Chao, M., Peñagarícano, J., Yan, Y., Moros, E. G., Corry, P. M., & Ratanatharathorn, V. (2011) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2011.01.021 · Cited by 10
Diffeomorphic Lung Registration Using Deep CNNs and Reinforced Learning
Onieva, J. O., Martí-Fuster, B., de la Puente, M. P., & Estépar, R. S. J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00946-5_28 · Cited by 10
A Novel Framework for 3D-2D Vertebra Matching
Yu, H., Fu, Y. L., Yu, H., Wei, Y., Wang, X., Jiao, J., Bramlet, M. T., Kesavadas, T., Shi, H., Wang, Z., et al. (2019) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr.2019.00029 · Cited by 8
Self-citationVariational Autoencoders for Feature Detection of Magnetic Resonance Imaging DataPreprint
Hjelm, D., Plis, S., & Calhoun, V. D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.06624 · Cited by 7
Zero Shot Learning for Multi-Modal Real Time Image RegistrationPreprint
Kori, A., & Krishnamurthi, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.06213 · Cited by 7
Liu, C., Ma, L., Lu, Z., Jin, X., & Xu, J. (2018) · Electronics Letters · doi:10.1049/el.2018.6713 · Cited by 6
On Reducing Negative Jacobian Determinant of the Deformation Predicted by Deep Registration NetworksPreprint
Kuang, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.00068 · Cited by 4
Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image SequencesPreprint
Qin, C., Bai, W., Schlemper, J., Petersen, S. E., Piechnik, S. K., Neubauer, S., & Rueckert, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.04066 · Cited by 3
Dubost, F., de Bruijne, M., Nardin, M., et al. (2019) · Cited by 2
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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
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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