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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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“deformation τ* in the set {τ, τ : (A, ΩA) → (S, ΩS)} of all transformations from the spatial domain of the source image (or atlas) (A, ΩA) to the subject space (S, ΩS), that minimizes the following energy functional (Sotiras et al., 2013; Vercauteren et al., 2007), E τ ( ) ≔ λζζ S, A◦τ ( ) + λρρ τ ( ), (1)”
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IDC-Net: Multi-stage Registration Network Using Intensity Adjustment, Dual-Stream and Cost Volume
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“Image registration is a well-studied image processing problem [19].”
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Gastroscopic Image Graph: Application to Noninvasive Multitarget Tracking under Gastroscopy
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“Although a wide range of registrationmethods have been developed over recent decades [21, 22], there is an absence of a gold standard for the evaluation of gastroscopic sequence registrations.”
BackgroundA Novel Multi-Modal Image Registration Method Based on Corners
Lv, G., Teng, S. W., & Lu, G. (2014) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2014.7008090 · Cited by 6
“I. INTRODUCTION Multi-modal image registration is an important research topic, especially in medical image analysis as images of a particular object are frequently captured using different types of capturing devices [1].”
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Metaxa, E., Iordanov, I., Maravelakis, E., & Papaharilaou, Y. (2016) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-016-1592-8 · Cited by 6
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BackgroundStatistical Shape and Appearance Models for Bone Quality Assessment
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Cardiac contraction motion compensation in gated myocardial perfusion SPECT: A comparative study
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A discretize–optimize approach for LDDMM registration
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Wang, H., Li, Q., Yuan, Y., Zhang, Z., Wang, K., & Zhang, H. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102455 · Cited by 6
Ruthven, M., Miquel, M. E., & King, A. P. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104290 · Cited by 6
“Traditional deformable registration methods establish nonlinear spatial correspondences (usually displacement vector fields) between two images by iteratively optimising a cost function [24].”
Wang, F., Xu, X., Yang, D., Chen, R. C., Royce, T. J., Wang, A. Z., Lian, J., & Lian, C. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3223405 · Cited by 6
“The atlas-based segmentation is commonly used in commercial software for radiotherapy planning [14], which leverages deformable image registration techniques [17] to align the label map(s) from template/atlas image(s) onto the image under segmentation.”
MethodQuasi-Conformal Hybrid Multi-modality Image Registration and its Application to Medical Image Fusion
Lam, K. C., & Lui, L. M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27857-5_72 · Cited by 6
“One main advantages of landmark-based method is the straightforward incorporation of medical expert during the registration process [2].”
MethodBise, R., Zheng, Y., Sato, I., & Toi, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46726-9_38 · Cited by 6
Torbati, N., & Ayatollahi, A. (2019) · International Journal of Wavelets Multiresolution and Information Processing · doi:10.1142/s0219691319500255 · Cited by 6
Pirpinia, K., Bosman, P. A. N., Sonke, J., van Herk, M., & Alderliesten, T. (2019) · Algorithms · doi:10.3390/a12050099 · Cited by 6
“Deformable image registration (DIR) [1], i.”
An Unsupervised Deep Learning Method for Diffeomorphic Mono-and Multi-modal Image Registration
Theljani, A., & Chen, K. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-39343-4_27 · Cited by 6
Reynosa-Guerrero, J., García-Huerta, J.-M., Vázquez‐Cervantes, A., Reyes-Santos, E., Pérez-Ramos, J.-L., & Jiménez‐Hernández, H. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113900 · Cited by 6
Predicting medical image registration error through independent directions
Saygılı, G. (2020) · Signal Image and Video Processing · doi:10.1007/s11760-020-01784-3 · Cited by 6
Strain Energy Decay Predicts Elastic Registration Accuracy From Intraoperative Data Constraints
Heiselman, J. S., & Miga, M. I. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3052523 · Cited by 6
“proposed, which are reviewed thoroughly in [6].”
Joint Learning for Deformable Registration and Malignancy Classification of Lung Nodules
Ghazipour, A., Veasey, B. P., Seow, A., & Amini, A. A. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433989 · Cited by 6
Ma, Y., Lu, C. H., Xiong, K., Zhang, W., & Yang, S. (2020) · Visual Computing for Industry Biomedicine and Art · doi:10.1186/s42492-020-00058-6 · Cited by 6
A taxonomy of mutual information in medical image registration
Borvornvitchotikarn, T., & Kurutach, W. (2016) · International Conference on Systems, Signals and Image Processing · doi:10.1109/iwssip.2016.7502712 · Cited by 6
“Both similarity measures are suitable for mono-modal image registration [3] [5] [6].”
Fast Mesh-Based Medical Image Registration
Baghaie, A., Yu, Z., & D’Souza, R. M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-14364-4_1 · Cited by 6
“Therefore the process of solving this problem consists of three component [6]: 1) a deformation model, 2) an objective function to be optimized and 3) an optimization method.”
MethodLinear and Deformable Image Registration with 3D Convolutional Neural NetworksPreprint
Christodoulidis, S., Sahasrabudhe, M., Vakalopoulou, M., Chassagnon, G., Revel, M., Mougiakakou, S. G., & Nikos Paragios, A. (2018) · arXiv (Cornell University) · doi:10.48350/120026 · Cited by 6
Fast optimization methods for image registration in adaptive radiation therapy
Qiao, Y. (2017) · Data Archiving and Networked Services (DANS) · Cited by 6
Zhang, J., Xie, X. N., Cheng, X., Li, T., Zhong, J., Hu, X., Sun, L., & Yan, H. (2024) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-23-1821 · Cited by 5
“Registration is a fundamental operation in medical image analysis, aiming to establish spatial correspondence between 2 (or more) images (9,10).”
Shur, J., Porta, N., Kafaei, L., Pendower, L., Mccall, J., Khan, N., Oyen, W., Koh, D.-M., & Johnston, E. (2025) · Radiology: Imaging Cancer · doi:10.1148/rycan.230147 · Cited by 5
“Nonrigid methods aim to account for changes in organ shape by deforming image sets with respect to each other (16).”
GVM-Net: A GNN-Based Vessel Matching Network for 2D/3D Non-Rigid Coronary Artery Registration
Chen, Y., Li, G., Li, C., Yu, W., Fan, Z., Bai, J., & Tu, S. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3540906 · Cited by 5
“These methods can be broadly classified into point-based, curve-based, and graph-based [30], [31], [32].”
Bakurov, I., Murphy, A., Ofria, C., & Banzhaf, W. (2025) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3712256.3726448 · Cited by 5
“Notice however, that correlation-based fitness has long been used in several other domains like image registration [38, 40] and quality assessment [3, 46], and even parametrization of statistical functions in space physics [31].”
Su, F., & Tian, Y. (2022) · Neuroscience · doi:10.1016/j.neuroscience.2022.03.034 · Cited by 5
Zang, Y., Wang, S., Guan, H., Peng, D., Chen, J., Chen, Y., & Delavar, M. R. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103642 · Cited by 5
Yan, Z., Ji, J., Ma, J., & Cao, W. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad2a96 · Cited by 5
RegPalm: Toward Large-Scale Open-Set Palmprint Recognition by Reducing Pattern Variance
Zhong, Y., Chai, W., Wang, L., Zheng, D., Fu, H., & Ma, H. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3593352 · Cited by 5
“The most widely used approach is to minimize a dissimilarity measure, with traditional approaches relying on iterative optimization strategies to calculate the mapping function progressively [66].”
Ali, S., Rohr, K., Axer, M., Amunts, K., Eils, R., & Wörz, S. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950550 · Cited by 5
“, [4]), and some approaches have addressed the registration of histological brain sections (e.”
BackgroundNumerical method for image registration model based on optimal mass transport
Chen, Y., & Wan, J. W. L. (2018) · Inverse Problems and Imaging · doi:10.3934/ipi.2018018 · Cited by 5
“A substantial discussion of existing methods can be found in [37, 45].”
Background
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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