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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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“…modern MRI acquisition methods now allow the rapid acquisition of large data volumes, combined with an excellent tissue contrast and high spatial resolution, image-registration techniques (Sotiras et al 2013) were recently employed to estimate on-line organ displacements from anatomical images.”
BackgroundDu, X., & Yu, J. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3271582 · Cited by 57
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Fast elastic registration of soft tissues under large deformations
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A Review of Point Feature Based Medical Image Registration
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“[6] conducted a survey regarding deformable medical image registration in which many nonrigid point set matching algorithms were discussed deliberately.”
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Estienne, T., Lerousseau, M., Vakalopoulou, M., Andres, E. A., Battistella, E., Carré, A., Chandra, S., Christodoulidis, S., Sahasrabudhe, M., Sun, R., et al. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.00017 · Cited by 43
“Among the different types of methods employed in medical applications, deformable or elastic registration is the most commonly used (Sotiras et al., 2013).”
MethodNon-iterative Coarse-to-Fine Transformer Networks for Joint Affine and Deformable Image Registration
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An Inexact Newton--Krylov Algorithm for Constrained Diffeomorphic Image Registration
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Che, T., Wang, X., Zhao, K., Zhao, Y., Zeng, D., Li, Q., Zheng, Y., Yang, N., Wang, J., & Li, S. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102740 · Cited by 52
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“Due to the lack of an image similarity measurement, it is also difficult to derive the driving force solely based on image intensity similarities for a physics-based model (Broit 1981, Sotiras et al 2013).”
BackgroundMethodFast Automatic Step Size Estimation for Gradient Descent Optimization of Image Registration
Qiao, Y., van Lew, B., Lelieveldt, B. P. F., & Staring, M. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2476354 · Cited by 51
“It has been used in clinical procedures including radiotherapy and image-guide surgery, and other general image analysis tasks, such as automatic segmentation [1]–[4].”
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“Many new deformation models, such as the flow and curvature models (Sotiras et al., 2013), follow the Demons’ method and are widely used in the medical image registration.”
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“These factors are often difficult to estimate accurately in practical applications because of the significant difference between the different target tissues or even the same target tissue in different individuals [9].”
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Benchmarking of Image Registration Methods for Differently Stained Histological Slides
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“ested readers to extensive reviews on medical image registration (Maintz and Viergever, 353 1998; Holden, 2008; Sotiras et al., 2013) for further information.”
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Mang, A., Gholami, A., Davatzikos, C. A., & Biros, G. (2018) · Optimization and Engineering · doi:10.1007/s11081-018-9390-9 · Cited by 49
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Dense registration of fingerprints
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RSegNet: A Joint Learning Framework for Deformable Registration and Segmentation
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“…medical images acquired from different viewpoints, different modalities, and even at different times, which is a fundamental procedure for various clinical tasks, such as multimodality image fusion, tumor growth process monitoring, atlas-based segmentation, and organ template creation [5].”
BackgroundFourier-Net: Fast Image Registration with Band-Limited Deformation
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“The deformable registration methods can be categorized into three classes with the matching criteria [61]: 1) geometric methods, 2) iconic methods, and 3) hybrid methods.”
MethodSpatial patterns and frequency distributions of regional deformation in the healthy human lung
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“Medical and biomedical image registration, [4], is an important branch of general image registration and a lot of effort has been invested over the last decades to refine the tools and techniques, [2].”
Sun, L., Shao, W., Wang, M., Zhang, D., & Liu, M. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2952079 · Cited by 48
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Cortical surface registration using unsupervised learning
Cheng, J., Dalca, A. V., Fischl, B. R., & Zöllei, L. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117161 · Cited by 46
“These aim to optimize a similarity metric between the target and the deformed source volumes, regularized by various energies (Sotiras et al., 2013).”
Zhang, S., Yang, K., Yang, Y., Luo, Y., & Wei, Z. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.03.004 · Cited by 45
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“The Thin-Plate Spline (TPS) has been widely used in image warping and other image processing operations that require the modeling of non-rigid deformations (Bookstein, 1989; Sotiras et al., 2013).”
BackgroundMethodNakao, M., Nakamura, M., Mizowaki, T., & Matsuda, T. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101829 · Cited by 45
Generative Adversarial Networks for MR-CT Deformable Image RegistrationPreprint
Tanner, C., Özdemir, F., Profanter, R., Vishnevskiy, V., Ender Konukoglu, A., & Göksel, O. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.07349 · Cited by 45
“Deformable Image Registration (DIR) is a challenging task and active field of research in medical image analysis [1].”
Biomechanical modeling and computer simulation of the brain during neurosurgery
Miller, K., Joldes, G. R., Bourantas, G. C., Warfield, S. K., Hyde, D. E., Kikinis, R., & Wittek, A. (2019) · International Journal for Numerical Methods in Biomedical Engineering · doi:10.1002/cnm.3250 · Cited by 44
“Therefore, evaluating the transform or its inverse at any other points (such as the centers of the voxels in the target image) will require spatial interpolation [95].”
BackgroundComparison between Weightbearing-CT semiautomatic and manual measurements in Hallux Valgus
de Carvalho, K. A. M., Walt, J. S., Ehret, A., Tazegul, T., Dibbern, K. N., Mansur, N. S. B., Lalevée, M., & Netto, C. D. C. (2022) · Foot and Ankle Surgery · doi:10.1016/j.fas.2022.02.014 · Cited by 43
AutoFuse: Automatic fusion networks for deformable medical image registration
Meng, M., FULHAM, M. J., Feng, D. D., Bi, L., & Kim, J. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2024.111338 · Cited by 43
“relies on deformable registration and this motivates the current research focus [2, 6].”
Learning image-based spatial transformations via convolutional neural networks: A review
Tustison, N. J., Avants, B., & Gee, J. C. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.05.037 · Cited by 45
Learning a Model-Driven Variational Network for Deformable Image Registration
Jia, X., Thorley, A., Chen, W., Qiu, H., Shen, L., Styles, I. B., Chang, H. J., Leonardis, A., de Marvao, A., O’Regan, D. P., et al. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3108881 · Cited by 49
Precision diagnostics based on machine learning-derived imaging signatures
Davatzikos, C. A., Sotiras, A., Yong Fan, A., Habes, M., Erus, G., Rathore, S., Bakas, S., Chitalia, R., Gastounioti, A., & Despina Kontos, B. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.04.012 · Cited by 42
“— Deformable image registration, i.e. the process of transforming one scan to another, is a fundamental component in many machine learning pipelines, since it maps data from multiple individuals to a standardized reference system, in which a machine learning model can be applied [41].”
BackgroundSelf-citationDeep Diffusion MRI Registration (DDMReg): A Deep Learning Method for Diffusion MRI Registration
Zhang, F., Wells, W. M., & O’Donnell, L. J. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3139507 · Cited by 42
Statistical Learning Methods for Neuroimaging Data Analysis with Applications
Hongtu Zhu, B., Li, T., & Zhao, B. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020722-100353 · Cited by 46
“We can further categorize structure learning methods into the image segmentation and registration processes (5–8 The image segmentation process for neuroimage data aims to label reconstructed neuroimaging data into meaningful subgroups for clinical and scientific tasks, including the quantification…”
Landmark-Matching Transformation with Large Deformation Via n-dimensional Quasi-conformal Maps
Lee, Y., Lam, K.-C., & Lok, M.-L. (2014) · Journal of Scientific Computing · doi:10.1007/s10915-015-0113-5 · Cited by 43
“Finding an optimal transformation between corresponding data, such as images or geometric shapes, is an important task in various fields, such as computer vision [61], computer graphics [6,22,24], video processing [38,45,59,60] and medical imaging [1,25,28,29,32,49,55].”
BackgroundA Meshfree Representation for Cardiac Medical Image Computing
Zhang, H., Gao, Z., Xu, L., Yu, X., Wong, K. C. L., Liu, H., Zhuang, L., & Shi, P. (2018) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2018.2795022 · Cited by 41
“For examples, there are a large number of meshbased computational strategies in the past, including cardiac image segmentation [12]–[19], material properties estimation [20]–[24], motion and deformation analysis [25]–[33], and cardiac image registration [34]–[36].”
BackgroundGaussian field estimator with manifold regularization for retinal image registration
Wang, J., Chen, J., Xu, H., Zhang, S., Mei, X., Huang, J., & Ma, J. (2018) · Signal Processing · doi:10.1016/j.sigpro.2018.12.004 · Cited by 41
Wodziński, M., & Müller, H. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105799 · Cited by 41
Wu, Y., Jiahao, T. Z., Wang, J., Yushkevich, P. A., Hsieh, M. A., & Gee, J. C. (2022) · 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/cvpr52688.2022.02014 · Cited by 47
“While there is usually no ground-truth for the optimal transformation, image registration is usually formulated according to and evaluated on three criteria: the accuracy of matching the source and target images in terms of a pre-defined similarity metric, the regularity of the transformation to ensure that it is well…”
BackgroundNon-iterative Coarse-to-Fine Registration Based on Single-Pass Deep Cumulative Learning
Meng, M., Bi, L., Feng, D. D., & Kim, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16446-0_9 · Cited by 40
“However, iterative optimization is time-consuming, especially for high-resolution 3D images such as brain Magnetic Resonance Imaging (MRI) [2], which limits its clinical applications as fast registration is widely desired in clinical practice.”
The numerical stability of transformation-based CT ventilation
Castillo, E. M., Castillo, R., Vinogradskiy, Y. Y., & Guerrero, T. M. (2017) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-016-1509-x · Cited by 40
“Considering the nature of anatomicalmotion,modernDIR methods are designed to produce diffeomorphic transformations that prohibit physically unrealistic tissue folding [32,33].”
BackgroundMethodMang, A., & Ruthotto, L. (2017) · SIAM Journal on Scientific Computing · doi:10.1137/17m1114132 · Cited by 39
“For a general insight into the area of image registration, its applications, and its formulation we refer to [53, 54, 63].”
BackgroundHuber, F. A., Schumann, P., von Spiczak, J., Wurnig, M. C., Klarhöfer, M., Finkenstaedt, T., Bedogni, A., & Guggenberger, R. (2019) · Investigative Radiology · doi:10.1097/rli.0000000000000617 · Cited by 40
“The CBCT dataset was used as the fixed image, whereas the MR dataset was the moving image.(21) The coregistration process was performed using proprietary medical image processing software (PMOD v.”
MethodA robust and interpretable deep learning framework for multi-modal registration via keypoints
Wang, A. Q., Yu, E., Dalca, A. V., & Sabuncu, M. R. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102962 · Cited by 39
Assessing tumor response after loco-regional liver cancer therapies: the role of 3D MRI
Chapiro, J., Lin, M., Durán, R., Schernthaner, R. E., & Geschwind, J.-F. H. (2014) · Expert Review of Anticancer Therapy · doi:10.1586/14737140.2015.978861 · Cited by 39
“The rigid approach uses anatomic landmarks/ structures such as bones, blood vessels or internal organs as markers in order to align two Digital Imaging and Communications in Medicine matrices by translation and rotation and without deforming individual voxel shape or size [20].”
An Automated Approach for Kidney Segmentation in Three-Dimensional Ultrasound Images
Marsousi, M., Plataniotis, K. N., & Stergiopoulos, S. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2580040 · Cited by 39
Linear and Deformable Image Registration with 3D Convolutional Neural Networks
Christodoulidis, S., Sahasrabudhe, M., Vakalopoulou, M., Chassagnon, G., Revel, M., Mougiakakou, S. G., & Nikos Paragios, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00946-5_2 · Cited by 39
“Through all the different registration methods used in medical applications, deformable registration is the one most commonly used due to its richness of description [15].”
MethodAn application independent review of multimodal 3D registration methods
Saiti, E., & Theoharis, T. (2020) · Computers & Graphics · doi:10.1016/j.cag.2020.07.012 · Cited by 42
“Registration of images has been extensively researched in the medical imaging domain, resulting in multiple reviews, focused on medical applications [11] or modalities [20] .”
BackgroundMethodInfluentialMaghsoudi-Ganjeh, M., Mariano, C. A., Sattari, S., Arora, H., & Eskandari, M. (2021) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2021.684778 · Cited by 39
“For instance, CT- and MR-based models (class 1) utilize convoluted and tedious deformable image registration (DIR), further challenged by the lack of a universal ground truth of lung nodules which necessitates expert-determined anatomical landmark detection and hinders model validation (Sotiras et al., 2013; Sarrut et…”
BackgroundSaleh, Z. H., Apte, A., Sharp, G. C., Shusharina, N., Wang, Y., Veeraraghavan, H., Thor, M., Muren, L. P., Rao, S. S. D., Lee, N. Y., et al. (2014) · Physics in Medicine and Biology · doi:10.1088/0031-9155/59/3/733 · Cited by 39
Probabilistic non-linear registration with spatially adaptive regularisation
Simpson, I., Cardoso, M. J., Marc Modat, A., Cash, D. M., Woolrich, M. W., Andersson, J., Schnabel, J. A., & Sebastien Ourselin, A. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.08.006 · Cited by 42
“Non-linear image registration is a fundamental tool in medical image analysis with a great many applications (Sotiras et al., 2013).”
BackgroundPredict brain MR image registration via sparse learning of appearance and transformation
Wang, Q., Kim, M., Shi, Y., Guorong Wu, A., & Shen, D. G. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.10.007 · Cited by 38
“…image registration methods in the literature, most of them regard image registration as a typical optimization problem (Rueckert and Schnabel, 2011; Sotiras et al., 2013), which (1) is favour of higher image similarity between the template and the subject images, (2) imposes certain regularization…”
Non-parametric Discrete Registration with Convex Optimisation
Heinrich, M. P., Papież, B. W., Schnabel, J. A., & Heinz Handels, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-08554-8_6 · Cited by 38
“Most deformable registration algorithms consist of three parts: similarity metric, optimisation strategy and transformation model, a comprehensive overview of the current literature is given in [21].”
BackgroundRobust CPD Algorithm for Non-Rigid Point Set Registration Based on Structure Information
Lei, P., Li, G., Xiao, M., & Xie, L. (2016) · PLoS ONE · doi:10.1371/journal.pone.0148483 · Cited by 38
“As a fundamental component of the computer vision field, point set registration is often used in medical image processing [1 – 3], pattern recognition [4], and remote sensing image processing [5, 6].”
MethodQuantifying Registration Uncertainty With Sparse Bayesian Modelling
Folgoc, L. L., Delingette, H., Criminisi, A., & Ayache, N. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2623608 · Cited by 38
“NON-RIGID image registration is an ill-posed task that supplements limited, noisy data with ‘inexact but useful’ prior knowledge to infer an optimal deformation between images of interest [1].”
Deep deformable registration: Enhancing accuracy by fully convolutional neural net
Ghosal, S., & Ray, N. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.05.022 · Cited by 38
“In these optimization-based methods, the de-formable transformation parameters are iteratively optimized over a scalar valued cost (e.g., SSD) representing the quality of registration [4].”
MethodCoupling brain-tumor biophysical models and diffeomorphic image registration
Scheufele, K., Mang, A., Gholami, A., Davatzikos, C. A., Biros, G., & Mehl, M. (2019) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2018.12.008 · Cited by 38
“We refer to (Modersitzki, 2004; Sotiras et al., 2013) for a general overview of medical image registration.”
Self-citationMultimodal 3D medical image registration guided by shape encoder–decoder networks
Blendowski, M., Bouteldja, N., & Heinrich, M. P. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-02089-8 · Cited by 39
Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology
Liu, X., Xing, F., Yang, C., Kuo, C. ‐. J., Fakhri, G. E., & Woo, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72084-1_8 · Cited by 38
GradICON: Approximate Diffeomorphisms via Gradient Inverse Consistency
Tian, L., Greer, H., Vialard, F., Kwitt, R., Estépar, R. S. J., Rushmore, J., Makris, N., Sylvain Bouix, A., & Niethammer, M. (2023) · 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/cvpr52729.2023.01734 · Cited by 37
“Image registration is a key component in medical image analysis to estimate spatial correspondences between image pairs [14, 53].”
BackgroundBarcelos, E. Z., Caminhas, W. M., Ribeiro, E., Pimenta, E. M., & Palhares, R. M. (2014) · Sensors · doi:10.3390/s141121950 · Cited by 37
“Here, a standard nonrigid registration method based on optical flow [25,26] is used.”
MethodOn the Applicability of Registration Uncertainty
Luo, J., Sedghi, A., Popuri, K., Cobzaş, D., Zhang, M., Preiswerk, F., Toews, M., Golby, A. J., Sugiyama, M., Wells, W. M., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32245-8_46 · Cited by 37
“These displacement vectors radiate from voxels on It and point to their candidate transformation locations on Is [2].”
BackgroundHuang, Y., Ahmad, S., Fan, J., Shen, D. G., & Yap, P. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101817 · Cited by 37
“Deformable registration methods aim at finding a non-linear transformation that establishes anatomical correspondences between a pair of images (Sotiras et al., 2013).”
BackgroundLiu, R., Li, Z., Fan, X., Zhao, C., Huang, H., & Luo, Z. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3115825 · Cited by 37
“tion method computes a dense correspondence between image pairs [2].”
MethodMozaffarzadeh, M., Moore, C., Barzegar-Golmoghani, E., Mantri, Y., Hariri, A., Jorns, A., Fu, L., Verweij, M. D., Orooji, M., de Jong, N., et al. (2021) · Biomedical Optics Express · doi:10.1364/boe.417345 · Cited by 37
Image-to-Graph Convolutional Network for 2D/3D Deformable Model Registration of Low-Contrast Organs
Nakao, M., Nakamura, M., & Matsuda, T. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3194517 · Cited by 37
“Deformable registration between 3D volumes poses a similar problem [10].”
BackgroundDencks, S., Lowerison, M. R., Hansen-Shearer, J. T. T., Shin, Y., Schmitz, G., Song, P., & Tang, M.-X. (2025) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2025.3543322 · Cited by 37
“A review paper on this can be found here [64].”
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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