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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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Su, Z., Wang, Y., Shi, R., Zeng, W., Sun, J., Luo, F., & Gu, X. D. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2408346 · Cited by 101
“[46], which gives a rigorous treatment for registration problem.”
Landmark- and Intensity-Based Registration with Large Deformations via Quasi-conformal Maps
Lam, K. C., & Lui, L. M. (2014) · SIAM Journal on Imaging Sciences · doi:10.1137/130943406 · Cited by 94
Nenoff, L., Amstutz, F., Murr, M., Archibald‐Heeren, B. R., Fusella, M., Hussein, M., Lechner, W., Zhang, Y., Sharp, G., & Osorio, E. M. V. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad0d8a · Cited by 99
Learning deformable registration of medical images with anatomical constraints
Mansilla, L., Milone, D. H., & Ferrante, E. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.01.023 · Cited by 94
“Since then, deformable image registration has been modelled in multiple ways (see [40] for a comprehensive description), most of them posing image registration as an optimization problem, which in its general form can be formulated as where I is the source (moving) image, J is the target (fixed)…”
BackgroundImage Registration Based on Autocorrelation of Local Structure
Li, Z., Mahapatra, D., Tielbeek, J. A. W., Stoker, J., A, L. J. V. V., & Vos, F. M. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2455416 · Cited by 92
“[1].”
MethodCNN-based lung CT registration with multiple anatomical constraints
Hering, A., Häger, S., Moltz, J. H., Leßmann, N., Heldmann, S., & van Ginneken, B. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102139 · Cited by 89
“This is a fundamental step for many medical image analysis tasks and has been an active field of research for decades (Maintz and Viergever, 1998; Sotiras et al., 2013).”
Machine learning based liver disease diagnosis: A systematic review
Khan, R. A., Luo, Y., & Wu, F. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.138 · Cited by 92
Image-and-Spatial Transformer Networks for Structure-Guided Image Registration
Lee, M. C. H., Oktay, O., Schuh, A., Schaap, M., & Glocker, B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32245-8_38 · Cited by 87
Viceconti, M., Vos, M. D., Mellone, S., & Geris, L. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3323688 · Cited by 85
“We use statistical atlas techniques [18] to calculate for each time point the average body shape and then calculate the transformation of each dataset to this average body shape.”
MethodRobust and efficient linear registration of white-matter fascicles in the space of streamlines
Garyfallidis, E., Ocegueda, O., Wassermann, D., & Descoteaux, M. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.016 · Cited by 84
“…bundle back to the volume grid by binarizing the streamlines, or creating track density images and then use classical scalarbased registration algorithms, e.g., Mean Squared Differences (MSD) or cross correlation (CC) or mutual information (MI) (Sotiras et al., 2013). b) Point-based registration.”
BackgroundMethodInfluentialMulti-modal automatic montaging of adaptive optics retinal images
Chen, M., Cooper, R. F., Han, G., Gee, J. C., Brainard, D. H., & Morgan, J. I. W. (2016) · Biomedical Optics Express · doi:10.1364/boe.7.004899 · Cited by 82
Pace, D. F., Aylward, S., & Niethammer, M. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2274777 · Cited by 82
“T HE GOAL of deformable image registration [1] is to establish correspondence, i.”
BackgroundSymReg-GAN: Symmetric Image Registration with Generative Adversarial Networks
Zheng, Y., Sui, X., Jiang, Y., Che, T., Zhang, S., Yang, J., & Li, H. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3083543 · Cited by 80
Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration
Krebs, J., Mansi, T., Mailhé, B., Ayache, N., & Delingette, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00889-5_12 · Cited by 78
“Furthermore, important properties like symmetry or diffeormorphisms [9] are still missing in DL-based approaches.”
BackgroundInfluentialLin, B., Sun, Y., Qian, X., Goldgof, D., Gitlin, R., & You, Y. (2016) · The international journal of medical robotics + computer assisted surgery : MRCAS · doi:10.1002/rcs.1661 · Cited by 87
“In the research community, multiple survey papers (7,8) on deformable medical image registration have been presented.”
BackgroundOptimal Transport for Diffeomorphic Registration
Feydy, J., Charlier, B., Vialard, F., & Gabriel Peyré, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66182-7_34 · Cited by 78
“…by minimizing an energy which is the sum of two terms: a data fidelity term which quantifies how well the source data has been aligned with the target data; a regularization term on the deformation, which is necessary to make the registration problem well-posed, see [14] for a detailed overview.”
BackgroundUnsupervised Deep Learning for Bayesian Brain MRI Segmentation
Dalca, A. V., Yu, E., Golland, P., Fischl, B. R., Sabuncu, M. R., & Iglesias, J. E. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32248-9_40 · Cited by 77
ModeT: Learning Deformable Image Registration via Motion Decomposition Transformer
Wang, H., Ni, D., & Wang, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43999-5_70 · Cited by 76
“Deformable image registration has always been an important focus in the society of medical imaging, which is essential for the preoperative planning, intraoperative information fusion, disease diagnosis and follow-ups [10,22].”
BackgroundMulti‐atlas based representations for Alzheimer's disease diagnosis
Min, R., Guorong Wu, A., Cheng, J., Wang, Q., Shen, D. G., & Initiative, T. A. D. N. (2014) · Human Brain Mapping · doi:10.1002/hbm.22531 · Cited by 79
“More recently, thanks to the substantial development of deformable image registration techniques in the last decade [Sotiras et al., 2013; Shen et al., 1999; Tang et al., 2009; Xue et al., 2006; Yap et al., 2009], automatic spatial normalization has become the fundamental step in morphometric pattern analysis, which a…”
BackgroundDeformable image registration by combining uncertainty estimates from supervoxel belief propagation
Heinrich, M. P., Simpson, I., Papież, B. W., Brady, M., & Schnabel, J. A. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.09.005 · Cited by 77
“12 To study the functionality or anatomical variability of human years (see (Sotiras et al., 2013) for an overview).”
BackgroundStudy on the improved fuzzy clustering algorithm and its application in brain image segmentation
Ren, T., Wang, H., Feng, H., Xu, C., Liu, G., & Pan, D. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105503 · Cited by 74
An overview of deep learning methods for image registration with focus on feature-based approaches
Kuppala, K., Sandhya, B., & Barige, T. R. (2020) · International Journal of Image and Data Fusion · doi:10.1080/19479832.2019.1707720 · Cited by 74
“2017) or for calculating similarity (Sotiras et al .”
BackgroundInfluentialHamiltonian Systems and Optimal Control in Computational Anatomy: 100 Years Since D'Arcy Thompson
Miller, M. I., Trouvé, A., & Laurent Younes, B. (2015) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071114-040601 · Cited by 72
U-ReSNet: Ultimate Coupling of Registration and Segmentation with Deep Nets
Estienne, T., Vakalopoulou, M., Christodoulidis, S., Battistella, E., Lerousseau, M., Carré, A., Klausner, G., Sun, R., Robert, C., Mougiakakou, S. G., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32248-9_35 · Cited by 72
Anatomical Attention Guided Deep Networks for ROI Segmentation of Brain MR Images
Sun, L., Shao, W., Zhang, D., & Liu, M. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2962792 · Cited by 72
“Generally, multi-atlas based segmentation meth-ods consist of two key steps, i.e. , 1) image registration [39]–[42] and 2) label fusion .”
BackgroundDisentangle, Align and Fuse for Multimodal and Semi-Supervised Image Segmentation
Chartsias, A., Papanastasiou, G., Wang, C., Semple, S., Newby, D. E., Dharmakumar, R., & Tsaftaris, S. A. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3036584 · Cited by 72
“This has been successful in the brain (see Section II-B) but precise multimodal registration needs modality independent metrics [4] and the brain remains static within an imaging session, whereas the heart is constantly moving.”
BackgroundSurvey of Non-Rigid Registration Tools in Medicine
Keszei, A. P., Berkels, B., & Deserno, T. M. (2016) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-016-9915-8 · Cited by 70
“registration methods [18] classifying methods on the basis of the core registration components: (i) deformation models, (ii) matching criteria, and (iii) optimization.”
MethodNon-rigid image registration using fully convolutional networks with deep self-supervisionPreprint
Li, H., & Yong Fan, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.00799 · Cited by 70
“ed image similarity measures. As the image registration problem is an ill-posed problem, regularization techniques are usually used to obtained smooth and physically plausible spatial transformations [1, 2]. In general, the optimization based image registration problem is formulated as min 𝐷𝑣 −�(𝐼𝑓(𝑣),𝐼�(𝐷𝑣∘𝑣))+𝜆�(𝐷𝑣)…”
BackgroundMethodInfluentialRobust Feature Matching for Remote Sensing Image Registration via Linear Adaptive Filtering
Jiang, X., Ma, J., Fan, A., Xu, H., Lin, G., Lü, T., & Tian, X. S. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3001089 · Cited by 68
“…between two or more images is a critical and fundamental process and has been widely used in various fields, including computer vision [14], [18]–[21], pattern recognition [22]–[24], image analysis [4], [25], security [5], [26], and especially in the field of remote sensing [3], [26]–[28].”
BackgroundLearning Dual Transformer Network for Diffeomorphic Registration
Zhang, Y., Pei, Y., & Zha, H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87202-1_13 · Cited by 66
Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration
Meng, M., Feng, D. D., Bi, L., & Kim, J. (2024) · 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/cvpr52733.2024.00921 · Cited by 67
“Medical image registration is a fundamental requirement for medical image analysis and has been an active research focus for decades [1, 2].”
BackgroundPreparing fMRI Data for Statistical Analysis
Nieto-Castañón, A. (2025) · Neuromethods · doi:10.1007/978-1-0716-4438-6_6 · Cited by 66
Devine, J., Aponte, J. D., Katz, D. C., Liu, W., Vercio, L. L., Forkert, N. D., Marcucio, R., Percival, C. J., & Hallgrímsson, B. (2020) · Evolutionary Biology · doi:10.1007/s11692-020-09508-8 · Cited by 65
“While registration-derived landmarks are appealing for biology and morphometrics, among other disciplines, their anatomical precision depends on the integrity of the deformable registration (Sotiras et al. 2013).”
BackgroundKubíček, J., Tomanec, F., Černý, M., Vilímek, D., Kalová, M., & Oczka, D. (2019) · Sensors · doi:10.3390/s19235199 · Cited by 68
“During this procedure, two or more individual images are matched and aligned into one single output image which provides more valuable information [75,76].”
BackgroundGhose, S., Holloway, L. C., Lim, K., Chan, P., Veera, J., Vinod, S. K., Liney, G. P., Greer, P. B., & Dowling, J. (2015) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2015.04.006 · Cited by 65
“Normalized mutual information (NMI) [30] as a similarity metric is maximized to achieve the registration.”
BackgroundMethodAutomatic hyoid bone detection in fluoroscopic images using deep learning
Zhang, Z., Coyle, J. L., & Sejdić, E. (2018) · Scientific Reports · doi:10.1038/s41598-018-30182-6 · Cited by 67
“Traditional methods usually use sliding windows for region proposal, histograms of gradient orientation (HoG) or scale-invariant feature transform (SIFT) as feature extraction 28,29 , and support vector machine (SVM) and Boosting methods as classifiers 30,31 .”
Method
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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