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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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BackgroundImage registration using machine and deep learning
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Image Registration Based on Low Rank Matrix: Rank-Regularized SSD
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Anatomically-adaptive multi-modal image registration for image-guided external-beam radiotherapy
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“A feasible solution for automatic estimation of changes in the shape and location of organ and pathological tissue boundaries over the course of the treatment is multi-modal deformable image registration (DIR) (Hill et al 2001, Mani and Arivazhagan 2013, Sotiras et al 2013).”
MethodDeep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper
Vinodkumar, P. K., Karabulut, D., Avots, E., Ozcinar, C., & Anbarjafari, G. (2024) · Entropy · doi:10.3390/e26030235 · Cited by 30
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Image fusion pitfalls for cranial radiosurgery
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Deformable Image Registration Using Functions of Bounded Deformation
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Framework for shape analysis of white matter fiber bundles
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Image Registration of Satellite Imagery with Deep Convolutional Neural Networks
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An Effective Non-rigid Image Registration Method Based on Active Demons Algorithm
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“Non-rigid image registration is one of the hottest research topics in recent years [1].”
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BackgroundNonlinear metric learning for kNN and SVMs through geometric transformations
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Unsupervised deep learning-based medical image registration: a survey
Duan, T., Chen, W., Ruan, M., Zhang, X., Shen, S., & Gu, W. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad9e69 · Cited by 24
Mathematical modeling in radiotherapy for cancer: a comprehensive narrative review
Zheng, D., Preuss, K., Milano, M. T., He, X., Gou, L., Shi, Y., Marples, B., Wan, R., Yu, H., Du, H., et al. (2025) · Radiation Oncology · doi:10.1186/s13014-025-02626-7 · Cited by 27
“…method and B-spline approximation have been the key methods that facilitate deformable image registration, which is critical when using various diagnostic imaging modalities that need to be fused to simulation scans (generally CT-based but also MRI-based with some commercial systems) [10–12].”
Zaki, G., Plishker, W. L., Li, W., Lee, J., Quon, H., Wong, J., & Shekhar, R. (2016) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2016.2597838 · Cited by 21
“DIR has a number of applications in radiation therapy [2], [3].”
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“The NCC is used because it is the most widely applied similarity measure for unimodal, medical image registration [16].”
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“32 In this study, we aimed to train a deep neural network to replace the subject-specific iterative optimization with a global optimization in the training data.”
MethodLocal All-Pass filters for optical flow estimation
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“This motion is known as the optical flow [1] and is utilised in a wide range of applications such as computer vision, biology [2, 3] and medical imaging [4, 5].”
BackgroundGuo, F., Svenningsen, S., Kirby, M., Capaldi, D. P. I., Sheikh, K., Fenster, A., & Párraga, G. (2017) · Medical Physics · doi:10.1002/mp.12160 · Cited by 21
A Statistically Representative Atlas for Mapping Neuronal Circuits in the Drosophila Adult Brain
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“However, in brain imaging of humans and other mammals, a wider range of transformation models have been proposed for image registration (Gholipour et al., 2007; Sotiras et al., 2013).”
BackgroundHering, A., Kuckertz, S., Heldmann, S., & Heinrich, M. P. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-02068-z · Cited by 21
“This is a fundamental step for many medical image analysis tasks and has been a very active field of research for decades [21].”
BackgroundFDRN: A fast deformable registration network for medical images
Sun, K., & Simon, S. (2021) · Medical Physics · doi:10.1002/mp.15011 · Cited by 21
“nd L are dened as L main= D(F h(x);˚ hM h(x)) + 1R(˚ h); L aux= D (F lx );˚ M )) + 2R˚ ; (3) where Ddenotes the data delity term and we adopt normalized cross-correlation (NCC) as expressed in Eq. (4) to quantify the similarity measure. ˚ h and ˚ l indicate respectively the deformation eld at the high and low resoluti…”
Joint affine and deformable three‐dimensional networks for brain MRI registration
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“, the problem to establish a mapping between two given shapes) is closely related to surface parametrization [54].”
BackgroundA Parallel Nonrigid Registration Algorithm Based on B-Spline for Medical Images
Du, X., Dang, J., Wang, Y., Wang, S., & Lei, T. (2016) · Computational and Mathematical Methods in Medicine · doi:10.1155/2016/7419307 · Cited by 20
“The process involves a reference image and a moving image; the moving image is deformed by a spatial transformation to align with the reference image [1].”
Wang, L., Chen, Z., Chalasani, P., Yasin, R., Kazanzides, P., Taylor, R. H., & Simaan, N. (2017) · Journal of Mechanisms and Robotics · doi:10.1115/1.4035684 · Cited by 20
Three-dimensional reconstruction of In Vivo human lumbar spine from biplanar radiographs
Bennani, H., Brendan Mccane, A., & Cornwall, J. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.102011 · Cited by 20
Multi-strategy mutual learning network for deformable medical image registration
Zheng, Z., Cao, W., Duan, Y., Cao, G., & Lian, D. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.06.020 · Cited by 20
Hu, R. M., Yan, H., Nian, F., Mao, R., & Li, T. (2022) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-21-1194 · Cited by 20
Robust endoscopic image mosaicking via fusion of multimodal estimation
Li, L., Mazomenos, E. B., Chandler, J. H., Obstein, K. L., Valdastri, P., Stoyanov, D. V., & Vasconcelos, F. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102709 · Cited by 20
Unsupervised deep learning registration model for multimodal brain images
Abbasi, S., Mehdizadeh, A., Boveiri, H. R., Mosleh‐Shirazi, M. A., Javidan, R., Khayami, R., & Tavakoli, M. (2023) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.14177 · Cited by 20
“4 For instance, in radiotherapy, a fully marked and annotated CT image is inputted into the Treatment Planning System (TPS) for treatment planning.”
BackgroundMethodComparison of consecutive and restained sections for image registration in histopathology
Lotz, J., Weiss, N., van der Laak, J. A. W. M., & Heldmann, S. (2023) · Journal of medical imaging · doi:10.1117/1.jmi.10.6.067501 · Cited by 20
Image descriptors in radiology images: a systematic review
Nogueira, M. A., Abreu, P. H., Martins, P., Machado, P., Duarte, H., & Santos, J. A. M. (2016) · Artificial Intelligence Review · doi:10.1007/s10462-016-9492-8 · Cited by 20
“We also opted not to include descriptor-based medical image registration techniques, since there are already several surveys covering this topic (e.g., Sotiras et al. 2013; Zitová and Flusser 2003 or Oliveira and Tavares 2014).”
MethodA visual analytics perspective on shape analysis: State of the art and future prospects
Hermann, M., & Klein, R. (2015) · Computers & graphics · doi:10.1016/j.cag.2015.08.008 · Cited by 20
Wavelet transform based new interpolation technique for satellite image resolution enhancement
Rasti, P., Lüsi, I., Demirel, H., Kiefer, R., & Anbarjafari, G. (2014) · IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES) · doi:10.1109/icares.2014.7024405 · Cited by 20
“Registration is one of the basic and the principal subjects in the image processing and there are various algorithms for it [16-19].”
BackgroundMulti-atlas automatic positioning of anatomical landmarks
Jacinto, H., Valette, S., & Prost, R. (2017) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2017.11.015 · Cited by 20
Automatic Multiorgan Segmentation via Multiscale Registration and Graph Cut
Kéchichian, R., Valette, S., & Desvignes, M. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2851780 · Cited by 20
“A review of keypoint-based medical image registration can be found in [45].”
BackgroundHermann, M., Schunke, A. C., Schultz, T., & Klein, R. (2015) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2015.2467198 · Cited by 20
Casamitjana, A., Mancini, M., Robinson, E., Peter, L., Annunziata, R., Althonayan, J. I. M., Crampsie, S., Blackburn, E., Billot, B., Atzeni, A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.05.579016 · Cited by 20
Development and validation of a longitudinal soft-tissue metastatic lesion matching algorithm
Santoro-Fernandes, V., Huff, D. T., Scarpelli, M., Perk, T., Albertini, M., Perlman, S., Yip, S., & Jeraj, R. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac1457 · Cited by 16
Wang, D., Pan, Y., Durumeric, O. C., Reinhardt, J. M., Hoffman, E. A., Schroeder, J., & Christensen, G. E. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102434 · Cited by 19
Supervised Deep Learning for Head Motion Correction in PET
Zeng, T., Zhang, J., Revilla, E. M., Lieffrig, E. V., Fang, X., Lu, Y., & Onofrey, J. A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16440-8_19 · Cited by 20
Liang, X., Lin, S., Liu, F., Schreiber, D. A., & Yip, M. C. (2023) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2023.3280463 · Cited by 19
“Recent comprehensive reviews of DIR algorithms can be found in [9], [10], and [11].”
BackgroundMEBRAINS 1.0: A new population-based macaque atlas
Balan, P. F., Zhu, Q., Li, X., Niu, M., Rapan, L., Funck, T., Wang, H., Bakker, R., Palomero‐Gallagher, N., & Vanduffel, W. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00077 · Cited by 19
“This approach has been shown to be a more robust method of registering medical images ( Klein et al., 2009; Sotiras et al., 2013).”
MethodMueller, K., Maier, A., Schwemmer, C., Lauritsch, G., Buck, S. D., Wielandts, J.-Y., Joachim Hornegger, A., & Fahrig, R. (2014) · Physics in Medicine and Biology · doi:10.1088/0031-9155/59/12/3121 · Cited by 19
Onofrey, J. A., Papademetris, X., & Staib, L. H. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2404572 · Cited by 19
“A large number of algorithms have been proposed for non-rigid image registration [3].”
BackgroundWang, G., Wang, Z., Chen, Y., Zhou, Q., & Zhao, W. (2016) · Information Sciences · doi:10.1016/j.ins.2016.08.041 · Cited by 19
Self-similarity inspired local descriptor for non-rigid multi-modal image registration
Zhu, F., Ding, M., & Zhang, X. (2016) · Information Sciences · doi:10.1016/j.ins.2016.08.031 · Cited by 19
Cloud-Based Benchmarking of Medical Image Analysis
Grünberg, K., Jiménez–del–Toro, O. A., András, J., Langs, G., Fernandez, T. S., Winterstein, M., Weber, M., & Krenn, M. (2017) · Springer eBooks · doi:10.1007/978-3-319-49644-3 · Cited by 21
Band-Limited Stokes Large Deformation Diffeomorphic Metric Mapping
Hernández, M. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2815346 · Cited by 19
“In LDDMM, diffeomorphisms are generated by time-varying smooth flows of vector fields in V , v ∈ L2([0, 1], V ).”
BackgroundMethodInfluentialLi, D., Zhong, W., Deh, K., Nguyen, T. D., Prince, M. R., Wang, Y., & Spincemaille, P. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2880733 · Cited by 19
Kugler, M., Goto, Y., Tamura, Y., Kawamura, N., Kobayashi, H., Yokota, T., Iwamoto, C., Ohuchida, K., Hashizume, M., Shimizu, A., et al. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-02019-8 · Cited by 19
Image-Driven Biophysical Tumor Growth Model Calibration
Scheufele, K., Subramanian, S., Mang, A., Biros, G., & Mehl, M. (2020) · SIAM Journal on Scientific Computing · doi:10.1137/19m1275280 · Cited by 19
“Classical image registration [39, 50] assumes that the input images (template and reference)”
BackgroundAutomatic large quantity landmark pairs detection in 4DCT lung images
Fu, Y., Wu, X., Thomas, A. M., Li, H. H., & Yang, D. (2019) · Medical Physics · doi:10.1002/mp.13726 · Cited by 19
“f ap1, bp2 = f ap1 + w, bp2 + w withw = − 1, 0, 1 (2)”
Image reconstruction through metamorphosis
Gris, B., Chen, C., & Öktem, O. (2019) · Inverse Problems · doi:10.1088/1361-6420/ab5832 · Cited by 19
“n needs to be recovered from noisy time series of measured data. An important case is when the only time dependency is that of the object. Spatiotemporal imaging occurs in medical imaging, see, e.g., [23] for a survey of organ motion models. It is particular relevant for techniques like positron emission tomography (P…”
BackgroundA Deep Network for Joint Registration and Reconstruction of Images with Pathologies
Han, X., Shen, Z., Xu, Z., Bakas, S., Akbari, H., Bilello, M., Davatzikos, C. A., & Niethammer, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59861-7_35 · Cited by 19
Self-citationSpace deformation based path planning for Mobile Robots
Ahmed, A., Maged, A., Soliman, A. M. A., El-Hussieny, H., & Magdy, M. (2021) · ISA Transactions · doi:10.1016/j.isatra.2021.08.019 · Cited by 19
RISM: Single-Modal Image Registration via Rank-Induced Similarity Measure
Ghaffari, A., & Fatemizadeh, E. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2479462 · Cited by 19
“MAGE registration, the spatially varying alignment of two images, is an important pre-process in many applications, e.g., remote sensing [1], computer-assisted surgery [2], and medical image analysis and processing [3].”
BackgroundGuiding multimodal registration with learned optimization updates
Gutiérrez-Becker, B., Mateus, D., Peter, L., & Navab, N. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.05.002 · Cited by 19
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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