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Deformable Medical Image Registration: A Survey
Open accessAbstract
Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.
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“deformable image registration (Sotiras et al., 2013) is a core component of all MAS methods, and the quality of individual registrations has a very high impact on the accuracy of the final segmentation.”
From MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection (2016) · cites this paper as Sotiras et al., 2013
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BackgroundClinically Deployed Computational Assessment of Multiple Sclerosis Lesions
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“Depending on the imaging modality and the specific prior knowledge of the object to register, a wide variety of methods have been considered in the literature (for an overview see [6, 7]).”
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BackgroundDeep Learning Point Cloud Registration based on Distance Features
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BackgroundNonrigid medical image registration with locally linear reconstruction
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Approximationorder of the lap optical flow algorithm
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Ahn, I. J., Kim, J. H., Chang, Y., Nam, W. H., & Ra, J. B. (2016) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/tns.2016.2611624 · Cited by 11
“To determine the optimum transformation parameters via iteration, we adopt the Gauss-Newton method [43].”
MethodWu, Y., Guorong Wu, A., Wang, L., Munsell, B. C., Wang, Q., Lin, W., Feng, Q., Chen, W., & Shen, D. G. (2015) · Medical Physics · doi:10.1118/1.4922393 · Cited by 11
Mathematical Approaches for Medical Image Registration
Zitová, B. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.99990-2 · Cited by 11
Symmetric Volume Maps: Order-invariant Volumetric Mesh Correspondence with Free Boundary
Abulnaga, S. M., Stein, O., Golland, P., & Solomon, J. (2022) · ACM Transactions on Graphics · doi:10.1145/3572897 · Cited by 10
TS-Net: Two-stage deformable medical image registration network based on new smooth constraints
Song, L., Ma, M., & Liu, G. (2023) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2023.01.013 · Cited by 10
CraNeXt: Automatic Reconstruction of Skull Implants With Skull Categorization Technique
Kesornsri, T., Asawalertsak, N., Tantisereepatana, N., Manowongpichate, P., Lohwongwatana, B., Puncreobutr, C., Achakulvisut, T., & Vateekul, P. (2024) · IEEE Access · doi:10.1109/access.2024.3415173 · Cited by 10
“To improve the completeness of the implants and complex defective regions, future work could investigate the use of shape priors or topology-aware loss functions to ensure that the predicted implants fully encompass the defective areas [42], [43].”
Artificial intelligence in musculoskeletal applications: a primer for radiologists
Tong, M. W., Zhou, J., Akkaya, Z., Majumdar, S., & Bhattacharjee, R. (2024) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.242830 · Cited by 12
“28 Recent literature has demonstrated the growing application of AI, in particular DL models, in image registration.”
BackgroundZhou, T., Gaskins, J., Poggie, J., & Bane, S. P. (2025) · Experiments in Fluids · doi:10.1007/s00348-024-03944-4 · Cited by 10
“…by DIR algorithms can be categorized into the following classes: (1) geometric transformations derived from physical models, (2) geometric transformations derived from interpolation theory, (3) knowledge-based geometric transformations, and (4) task-specific constraints (Sotiras et al. 2013).”
InfluentialA Multihierarchy Flow Field Prediction Network for Multimodal Remote Sensing Image Registration
Wang, W., Mu, K., & Liu, H. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3532939 · Cited by 10
Deep multispectral image registration network
Sui, X., Zheng, Y., Jiang, Y., Jiao, W., & Ding, Y. (2020) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2020.101815 · Cited by 10
Hernández, M. (2021) · Journal of Computational Science · doi:10.1016/j.jocs.2021.101470 · Cited by 10
Automated inter-device 3D OCT image registration using deep learning and retinal layer segmentation
Rivas-Villar, D., Motschi, A. R., Pircher, M., Hitzenberger, C. K. F., Schranz, M. W., Roberts, P. K., Schmidt-Erfurth, U. M., & Bogunović, H. (2023) · Biomedical Optics Express · doi:10.1364/boe.493047 · Cited by 10
A Multiscale Deformation Representation
Debroux, N., Guyader, C. L., & Vese, L. A. (2023) · SIAM Journal on Imaging Sciences · doi:10.1137/22m1510200 · Cited by 10
An unsupervised deep learning framework for large-scale lung CT deformable image registration
Zhao, Y., Xie, Z., Zhang, F., Liao, M., & Di, S. (2023) · Optics & Laser Technology · doi:10.1016/j.optlastec.2023.110298 · Cited by 10
Thoen, H., Ceelen, W. P., Boterberg, T., Daele, E. V., & Pattyn, P. (2015) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2015.03.012 · Cited by 10
“Non-rigid registration methods, which perform deforming operations with the use of vector mapping, might yield better results [34].”
BackgroundLü, B., Chen, Y., Park, J. C., Fan, Q., Kahler, D. L., & Liu, C. (2014) · Medical Physics · doi:10.1118/1.4903888 · Cited by 10
Augmented reality for breast tumors visualization
Ghaderi, M. A., Heydarzadeh, M., Nourani, M., Gupta, G., & Tamil, L. S. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591700 · Cited by 10
“Secondly, we didn’t do a comprehensive study on the accuracy as in this proof of concept research; several research studies have already worked on some aspects of this problem [21], [22], [23].”
ResultZhang, T., Huang, Z., Huang, Y., Wang, G., Sun, X., Sang, N., & Chen, W. (2016) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2016.09.012 · Cited by 10
Nonlinear image registration with bidirectional metric and reciprocal regularization
Ying, S., Li, D., Xiao, B., Peng, Y., Du, S., & Xu, M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0172432 · Cited by 10
Conze, P.-H., Tilquin, F., Noblet, V., Rousseau, F., Heitz, F., & Pessaux, P. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950567 · Cited by 10
“Such complex geometric transformations can be then derived either from physical models (fluid flow or diffusion models, flow of diffeomorphisms...) or interpolation theory (radial basis functions, free-form deformations...) [2].”
BackgroundNon-local Graph-Based Regularization for Deformable Image Registration
Papież, B. W., Szmul, A., Grau, V., Brady, J. M., & Schnabel, J. A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-61188-4_18 · Cited by 10
“Deformable image registration (DIR) algorithms developed for medical imaging applications generally suffer a trade-off between computational complexity and medical plausibility [1].”
BackgroundCompensating anisotropy in histological serial sections with optical flow-based interpolation
Lobachev, O., Steiniger, B. S., & Guthe, M. (2017) · Spring Conference on Computer Graphics · doi:10.1145/3154353.3154366 · Cited by 9
“With further processing (most notably: registration [Lobachev et al. 2017; Saalfeld et al. 2012; Sotiras et al. 2013]), a stack of serial sections can be treated as volume data.”
MethodPapież, B. W., Franklin, J. M., Heinrich, M. P., Gleeson, F. V., Brady, M., & Schnabel, J. A. (2018) · Journal of medical imaging · doi:10.1117/1.jmi.5.2.024001 · Cited by 10
“Deformable image registration (DIR) of medical image volumes is an essential component of many biomedical image analysis applications.(1) For example, computed tomography (CT)”
BackgroundMR-CBCT image-guided system for radiotherapy of orthotopic rat prostate tumors
Chiu, T., Arai, T. J., Campbell, J., III, Jiang, S. B., Mason, R. P., & Stojadinović, S. (2018) · PLoS ONE · doi:10.1371/journal.pone.0198065 · Cited by 10
Evaluation of a Learning-based Deformable Registration Method on Abdominal CT Images
Bhattacharjee, R., Heitz, F., Noblet, V., Sharma, S., & Sharma, N. (2020) · IRBM · doi:10.1016/j.irbm.2020.04.002 · Cited by 10
3D Reconstruction and Segmentation of Dissection Photographs for MRI-Free Neuropathology
Tregidgo, H. F. J., Casamitjana, A., Latimer, C. S., Kilgore, M. D., Robinson, E., Blackburn, E., Leemput, K. V., Fischl, B. R., Dalca, A. V., Donald, C. M., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59722-1_20 · Cited by 10
Luo, C., Franciosa, P., Ceglarek, D. J., Ni, Z., & Mo, Z. (2020) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2020.3035735 · Cited by 10
“The developed method utilizes freeform deformation (FFD) technique [45], [46] which is widely used in computer graphics [47], medical image processing [48], and computer animation [49].”
MethodXie, X., Song, Y., Ye, F., Yan, H., Wang, S., Zhao, X., & Dai, J. (2021) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-20-705 · Cited by 10
Learning-based Image Registration with Meta-Regularization
Safadi, E. A., & Song, X. B. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01078 · Cited by 10
“In traditional image registration, a moving image M is spatially transformed to be in anatomical correspondence with a fixed image F according to an image-similarity metric [1].”
MethodYang, T., Bai, X., Cui, X., Gong, Y., & Li, L. (2021) · Medical Physics · doi:10.1002/mp.15420 · Cited by 10
A data-driven analysis of global research trends in medical image: A survey
Fan, C., Hu, K., Yuan, Y., & Li, Y. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.10.047 · Cited by 10
Aganj, I., Reuter, M., Sabuncu, M. R., & Fischl, B. R. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.10.059 · Cited by 10
“Pairwise deformable registration is performed by maximizing some measure of similarity between the corresponding regions of the two images (for a survey, see (Sotiras et al., 2013)).”
Metadata
- Authors
- Sotiras, A., Davatzikos, C., & Paragios, N.
- Affiliations
- University of Pennsylvania (US)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2013-07
- Volume
- 32
- Issue
- 7
- Pages
- 1153-1190
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- PMC
- PMC3745275
- OpenAlex
- W2115167851
- Cited by
- 1,587
- References
- 539
- Citations by year
- 2026: 81 · 2025: 121 · 2024: 115 · 2023: 96 · 2022: 115 · 2021: 142 · 2020: 136 · 2019: 126 · 2018: 152 · 2017: 153 · 2016: 130 · 2015: 123 · 2014: 86 · 2013: 9
- FWCI
- 18.05
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- HAL (submittedVersion, other-oa)
- Funders
- National Institute on Aging (R01AG014971)
- National Institute of Mental Health (R01MH070365)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- deformable image registration, medical image registration, multimodal image fusion, statistical atlas, population models, anatomical variation
- MeSH terms
- Algorithms, Diagnostic Imaging, Humans, Image Processing, Computer-Assisted
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