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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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Dynamic meshing for deformable image registration
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Propagation of registration uncertainty during multi-fraction cervical cancer brachytherapy
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Minimal Information Exchange for Secure Image Hash-Based Geometric Transformations Estimation
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BackgroundMethodQuasiconformal model with CNN features for large deformation image registration
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Symmetry-based representation for registration of multimodal images
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“In conventional methods, deformable registration problems are posed in an optimization framework [1]–[3].”
Method4D-CBCT Registration with a FBCT-derived Plug-and-Play Feasibility Regularizer
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Inter-Phase 4D Cardiac MRI Registration With a Motion Prior Derived From CTA
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“In order to overcome this problem, nonrigid registration [7]–[13], and active contour methods [14]–[17] have been proposed for object tracking in”
BackgroundA novel contour-based registration of lateral cephalogram and profile photograph
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BackgroundFully Convolutional Network for Consistent Voxel-Wise Correspondence
Zhang, Y., Pei, Y., Guo, Y., Ma, G., Xu, T., & Zha, H. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6992 · Cited by 5
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BackgroundMethodRegularized directional representations for medical image registrationPreprint
Jaouen, V., Conze, P.-H., Dardenne, G., Bert, J., & Visvikis, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.15509 · Cited by 5
“Regarding the choice of the SM, normalized mutual information (NMI) [14], [15] has remained highly popular for more than two decades [11] due to its high success and its relative insen-”
Zhang, W., Cao, Y., Hu, X., Mi, J., Zhang, P., Sun, G., Mukhopadhyay, S. C., Li, Y., & Liu, Z. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107201 · Cited by 4
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Curvature-corrected retinal registration of diagnostic OCT with instrument-integrated OCT
Briel, M., Haide, L., Meyling, M., Saalfrank, B., Matten, P., Piccinelli, N., Kronreif, G., Spadea, M. F., Tagliabue, E., & Mathis-Ullrich, F. (2025) · Scientific Reports · doi:10.1038/s41598-025-28922-6 · Cited by 4
“In multi-modal registration, it is common practice to convert both modalities into a unified representation 15 .”
Multimodal image registration based on the expectation–maximisation methodology
Arce-Santana, E., Campos-Delgado, D. U., Reducindo, I., & Mejia-Rodriguez, A. R. (2017) · IET Image Processing · doi:10.1049/iet-ipr.2017.0234 · Cited by 4
“In fact, mutual information (MI) methods have outperformed most of the existing approaches and have become a reference in multimodal registration [3, 4].”
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“(B) 3D Endovaginal Ultrasound Imaging with 360 ̊ rotational transducer using a BK Ultrasound System (Flex Focus 500) [9].”
MethodmiRID: Multi-Modal Image Registration Using Modality-Independent and Rotation-Invariant Descriptor
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“Initially, mutual information (MI) has been extensively and successfully used in image processing to measure the intensity relationships [4,6].”
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“When constructing a DIR method, three key elements need to be defined: (i) the transformation model, (ii) the regularizer, and (iii) the similarity measure [1].”
BackgroundMethodTime multiscale regularization for nonlinear image registration
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“Real-time US nonrigid registration is a challenge in the medical imaging field [8], [9].”
BackgroundEnhancing 3D medical image registration with cross attention, residual skips, and cascade attention
Anwar, M., He, Z., & Cao, W. (2024) · Intelligent Data Analysis · doi:10.3233/ida-230692 · Cited by 4
“The majority of deformable registration algorithms in use today employ an iterative approach that involves fine-tuning a transformation by minimizing an associated energy function [21, 22].”
Key technologies and challenges in online adaptive radiotherapy for lung cancer
Dong, B., Zheng, S., Chen, K. S., Zhu, X., Huang, S., Jiang, X., Diao, W., Li, H., Jia, L., Chi, F., et al. (2024) · Chinese Medical Journal · doi:10.1097/cm9.0000000000003299 · Cited by 4
“[32] Some reports have shown that the biomechanical models of various tissue types may also improve the accuracy of DIR techniques.”
MMCL: Meta-mutual contrastive learning for multi-modal medical image fusion
Zhang, Y., Ma, C., Ding, H., & Zhu, Y. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104806 · Cited by 4
Diff-Def: Diffusion-Generated Deformation Fields for Conditional Atlases
Starck, S., Sideri‐Lampretsa, V., Kainz, B., Menten, M. J., Mueller, T. T., & Rueckert, D. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3595421 · Cited by 4
“Usually, the former employs deformable registration [10], where semantic regions of an image of a cohort and a reference image are aligned and averaged [10].”
Non-rigid registration of magnetic resonance imaging of brain
Chesseboeuf, C., Biermé, H., Dambrine, J., Guillevin, C., & Guillevin, R. (2015) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2015.7367172 · Cited by 4
“∫ C w −→ dl = ∫ D ∇× w = ∫ T2 (∇× w) (x)u(x) dx. (5) From (5) we remark that the computation of any current can be performed with an integral over the whole domain.”
BackgroundMethodInfluentialPenjweini, R., Kim, M. M., Dimofte, A., Finlay, J. C., & Zhu, T. C. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2211110 · Cited by 4
Fast reconstruction of image deformation field using radial basis function
Ručka, L., & Peterlík, I. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950719 · Cited by 4
“It is often used to limit the set of possible transformations to those which are physically feasible [3].”
BackgroundWodziński, M., Skalski, A., Kędzierawski, P., Kuszewski, T., & Ciepiela, I. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-66905-2_12 · Cited by 4
Feature-Based Non-rigid Registration of Serial Section Images by Blending Rigid Transformations
Kajihara, T., Funatomi, T., Kubo, H., Aoto, T., Makishima, H., Yamada, S., & Mukaigawa, Y. (2017) · Asian Conference on Pattern Recognition · doi:10.1109/acpr.2017.107 · Cited by 4
“The feature-based approach uses invariant local image features for robust matching, and there have been many studies of it in the image processing field [6], [12].”
MethodYan, P., Kodera, Y., & Shimamoto, K. (2017) · International Journal of Biomedical Imaging · doi:10.1155/2017/3457189 · Cited by 4
“One of the applications is the alignment of chest computed tomography (CT) images from the same subject and, in particular, of the lung and its internal structures for investigations, such as assessment of temporal structural changes or anatomical changes [1].”
BackgroundSpatially Consistent Supervoxel Correspondences of Cone-Beam Computed Tomography Images
Pei, Y., Yi, Y., Ma, G., Kim, T., Guo, Y., Xu, T., & Zha, H. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2829629 · Cited by 4
“See [3]–[5] for comprehensive surveys.”
Construction and validation of image-based statistical shape and intensity models of bone
Reyneke, C., Thusini, X., Douglas, T. S., Vetter, T. R., & Mutsvangwa, T. (2018) · Biennial South African Biomedical Engineering Conference (SAIBMEC) · doi:10.1109/saibmec.2018.8363176 · Cited by 4
“For a detailed discussion about deformable models in the context of medical image registration the reader is referred to [4].”
BackgroundA sparse-to-dense method for 3D optical flow estimation in 3D light-microscopy image sequences
Manandhar, S., Bouthémy, P., Welf, E. S., Roudot, P., & Charles Kervrann, A. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363728 · Cited by 4
“A related problem is 3D image registration in medical imaging as surveyed in [5].”
BackgroundMulti-Individual Mammographic Image Registration Based on Global-Local Integrated Transformations
Li, C., Chen, Z., & Zhang, H. (2018) · IEEE International Conference on Software Engineering and Service Science (ICSESS) · doi:10.1109/icsess.2018.8663854 · Cited by 4
Robust and Accurate 3D Curve to Surface Registration with Tangent and Normal Vectors
Min, Z., Zhu, D., & Meng, M. Q. ‐. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196923 · Cited by 4
“In the field of medical image computing (MIC), registration algorithms are used to fuse multi-modalities of the same organ [17].”
MethodGil, N., Lipton, M. L., & Fleysher, R. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117657 · Cited by 4
Fusion of multi-source retinal fundus images via automatic registration for clinical diagnosis
Dan, T., Hu, Y., Han, C., Fan, Z., Huang, Z., Zhang, B., Tao, G., Liu, B., Yu, H., & Cai, H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.05.091 · Cited by 4
“However, existing methods perform unsatisfactory results in retinal image registration for ill-https conditioned samples, such as severer spatial deformations, or appearing of micro-bleeding and plaques [6].”
MethodEvaluating Registrations of Serial Sections With Distortions of the Ground Truths
Lobachev, O., Funatomi, T., Pfaffenroth, A., Förster, R., Knudsen, L., Wrede, C., Guthe, M., Haberthür, D., Hlushchuk, R., Salaets, T., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3124341 · Cited by 4
“[9] survey non-rigid registration methods.”
Kornaropoulos, E. N., Zacharaki, E. I., Zerbib, P., Lin, C., Rahmouni, A., & Nikos Paragios, A. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3156009 · Cited by 4
Xie, X., Song, Y., Ye, F., Yan, H., Wang, S., Zhao, X., & Dai, J. (2022) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.13793 · Cited by 4
Dufresne, E., Fortun, D., Kremer, S., & Noblet, V. (2022) · Frontiers in Neuroimaging · doi:10.3389/fnimg.2022.1008128 · Cited by 4
Zhang, R., Wang, J., & Chen, C. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106806 · Cited by 4
Saha, S. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-8477-8_15 · Cited by 4
The use of image synthesis techniques in target and roi delineation in the upright position
Schreuder, A. N., Nikos Paragios, A., Kissick, M. W., Lis, M., Underwood, T. S. A., & Mackie, R. W. (2023) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.14079 · Cited by 4
NeuronAlg: An Innovative Neuronal Computational Model for Immunofluorescence Image Segmentation
Giacopelli, G., Migliore, M., & Tegolo, D. (2023) · Sensors · doi:10.3390/s23104598 · Cited by 6
Multi-Modal Image Registration Based on Phase Exponent Differences of the Gaussian Pyramid
Yan, X., Cao, Y., Yang, Y., & Yao, Y. (2023) · Remote Sensing · doi:10.3390/rs15245764 · Cited by 4
Forsberg, D., Gupta, A. B., Mills, C., MacAdam, B., Rosipko, B., Bangert, B. A., Coffey, M. D., Kosmas, C., & Sunshine, J. L. (2016) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-016-1506-0 · Cited by 4
Multiatlas Segmentation Using Robust Feature-Based Registration
Fejne, F., Landgren, M., Alvén, J., Ulén, J., Fredriksson, J., Larsson, V., Enqvist, O., & Kahl, F. (2017) · Springer eBooks · doi:10.1007/978-3-319-49644-3_12 · Cited by 4
“Image registration can be divided into two different approaches; feature-based and intensity-based registration, see the surveys [15, 28].”
BackgroundApplication of demons image registration algorithms in resected breast cancer lodge localization
Wodziński, M., Skalski, A., Ciepiela, I., Kuszewski, T., & Kędzierawski, P. (2017) · Signal Processing: Algorithms, Architectures, Arrangements, and Applications · doi:10.23919/spa.2017.8166900 · Cited by 4
“The most widely used are: intensity-based rigid or affine registration [2], ICP algorithm [3], B-Splines FFD [4], different types of Demons algorithm [5], [6], [7], [8], [9], elastic models [2], and TV-L1 optical flow [10].”
BackgroundMethodInfluentialUsing continuum robots for force-controlled semi autonomous organ exploration and registration
Yasin, R., Wang, L., Abah, C., & Simaan, N. (2018) · International Symposium on Medical Robotics · doi:10.1109/ismr.2018.8333309 · Cited by 4
“A summary and classification of deformable registration techniques is available in [1].”
BackgroundIqbal, T., Shah, S. K., Ullah, F., Mehmood, S., & Zeb, M. A. (2020) · Microscopy Research and Technique · doi:10.1002/jemt.23476 · Cited by 4
Tissue-specific deformable image registration using a spatial-contextual filter
Zhang, Y., Zhang, L., Court, L. E., Balter, P. A., Dong, L., & Yang, J. (2020) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2020.101849 · Cited by 4
“Most existing regularizations take advantage of generic priors, such as continuous, differentiable, diffeomorphic and incompressible constraints [11–13].”
BackgroundWang, Y., Ni, D., Qin, J., Lin, M., Xie, X., Xu, M. T., & Heng, P. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13410-9_12 · Cited by 4
“This method allows the reliable estimation of complex deformation fields by utilizing more informed priors relative to the biomechanical properties of the tissues [5].”
MethodA robust similarity measure for nonrigid image registration with outliers
Pszczółkowski, S., Zafeiriou, S. P., Ledig, C., & Rueckert, D. (2014) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2014.6867934 · Cited by 4
“A large number of different successful approaches have been proposed to the problem of nonrigid image registration [1].”
BackgroundChao, M., Yuan, Y., Sheu, R., Wang, K., Rosenzweig, K. E., & Lo, Y. (2015) · Technology in Cancer Research & Treatment · doi:10.1177/1533034615600569 · Cited by 4
“There are many deformable registration techniques.(18) However, most of them are whole image-based models.”
BackgroundComparison of image registration methods for composing spectral retinal images
Laaksonen, L., Claridge, E., Fält, P., Hauta‐Kasari, M., Uusitalo, H. M. T., & Lensu, L. T. (2017) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2017.03.003 · Cited by 4
A TV-l 1 based nonrigid image registration by coupling parametric and non-parametric transformation
Hu, W., Xie, Y., Li, L., & Zhang, W. (2015) · Machine Intelligence Research · doi:10.1007/s11633-014-0874-6 · Cited by 4
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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