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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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“78 For nonrigid registration algorithms, changes in region/volume of interest due to the registration method should be considered, and us-ing segmentation information for registration is recommended for precise registration.”
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“The purpose of image registration (Hill et al., 2001; Sotiras et al., 2013) is to deform the test image to the coordinate system of the reference image, so that the same point can be imaged at the same coordinate of the two images (Oliveira and Tavares, 2014).”
BackgroundDeformable cardiac surface tracking by adaptive estimation algorithms
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Recurrent inference machine for medical image registration
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A CNN‐transformer‐based unsupervised aware hierarchical network for medical image registration
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MRUCT: Mixed Reality Assistance for Acupuncture Guided by Ultrasonic Computed Tomography
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“The objective of image registration is to identify the optimal deformation field ϕ that aligns the template image with the corresponding anatomical structure on the target image [44].”
Multimodal 3D rigid image registration based on expectation maximization
Velázquez-Durán, M. J., Campos-Delgado, D. U., Arce-Santana, E., & Mejía-Rodríguez, A. (2019) · Health technology · doi:10.1007/s12553-019-00353-0 · Cited by 2
Cui, Z., Mahmoodi, S., Guy, M., Lewis, E. B., Havelock, T., Bennett, M. J., & Conway, J. H. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105232 · Cited by 3
“…target image (i.e. SPECT) is usually deteriorated by artefacts and outliers (e.g. images depicted with the presence of tumors or lesion tissue can be considered as outliers) [13], the main challenge on the MSCT/SPECT registration is to prevent the excessive deformation of source image (i.e. MSCT).”
BackgroundMethodSuman, A. A., Asikuzzaman, M., Webb, A. L., Perriman, D. M., Tahtalı, M., & Pickering, M. R. (2020) · IEEE Access · doi:10.1109/access.2020.3015504 · Cited by 3
“The choice of transformation has a large effect on the registration process [49], with the most appropriate one not known as a prior [50].”
BackgroundMethodBalanced multi-image demons for non-rigid registration of magnetic resonance images
Mesin, L. (2020) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2020.09.013 · Cited by 3
Multimodal MR image registration using weakly supervised constrained affine network
Wang, X., Mao, L., Huang, X., Xia, M., & Gu, Z. (2021) · Journal of Modern Optics · doi:10.1080/09500340.2021.1939897 · Cited by 3
Bouter, A., Alderliesten, T., & Bosman, P. A. N. (2021) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec45853.2021.9504840 · Cited by 3
“D eformable I mage R egistration Deformable Image Registration (DIR) [11] is a problem aimed at finding a non-rigid deformation that deforms a source image to match a target image as well as possible.”
BackgroundMethodSun, D., Zhang, Y., Pei, Y., Xu, T., & Zha, H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87202-1_16 · Cited by 3
Optimizing Loss Function for Uni-modal and Multi-modal Medical Registration
Li, Z., Fan, X., Liu, R., & Luo, Z. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-93046-2_23 · Cited by 3
Debroux, N., Guyader, C. L., & Vese, L. A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-75549-2_10 · Cited by 3
Ashfaq, M., Minallah, N., Frnda, J., & Beháň, L. (2022) · Symmetry · doi:10.3390/sym14081506 · Cited by 3
Ahmad, S., & Khan, M. F. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.09.022 · Cited by 3
“Thus, additional constraints are required to be incorporated into the registration process, in order to recover a smooth, non-singular and invertible transformation [18].”
Background2D/3D Non-rigid Image Registration by an Efficient Demons Approach
Mishra, A., Mondal, P., & Banerjee, S. (2014) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2014.100 · Cited by 3
“The models for nonrigid registration presented in [1], [2] can be grouped into two categories, namely, models which work on feature selection, and models which work on image intensity.”
BackgroundMutual information extremal optimization for multimodal medical image registration
Sanchez, P. P. C., Legal-Ayala, H., & Schaerer, C. E. (2014) · Latin American Computer Conference (CLEI) · doi:10.1109/clei.2014.6965101 · Cited by 3
“La investigación de la registración de imágenes médicas desde sus inicios hasta la actualidad ha pasado por diferentes abordajes, desde el manual hasta el automático [1], [2], [3].”
BackgroundNikos Paragios, A., & Nikos Komodakis, A. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.13 · Cited by 3
“Concerning deformable registration [66], which is the pillar of medical image analysis, in [67]2 we have introduced a low graphical model formulation coupled with efficient linear programming to efficiently and precisely address mono and multi-modal iconic registration [68].”
MethodWu, J., Ou, Y., Weinstein, S. P., Conant, E. F., Yu, N., Hoshmand, V., Keller, B. M., Ashraf, A. B., Rosen, M. A., DeMichele, A. M., et al. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2081938 · Cited by 3
“Image registration algorithms [9, 10] that aim to build voxel-wise anatomical correspondences, are able to automatically and efficiently quantify both tumor and normal breast tissue local changes over time, and therefore provide a potentially more attractive pathway (known as parametric response map[11-13]) to uncover…”
ResultSelf-citationAlderliesten, T., Bosman, P. A. N., Sonke, J., & Bel, A. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2042856 · Cited by 3
Physical Constraint Finite Element Model for Medical Image Registration
Zhang, J., Wang, J., Wang, X., Gao, X., & Feng, D. D. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140567 · Cited by 3
“However, it lacked a sound theoretical justification [9].”
BackgroundAn evaluation of image registration methods for chest radiographs
Engin, M., Oğul, H., Ağıldere, A. M., & Sümer, E. (2015) · SAI Intelligent Systems Conference · doi:10.1109/intellisys.2015.7361237 · Cited by 3
“Recovering the actual correspondences in image pairs acquired from subjects as different times is an important capability used for nodule matching and cancer nodule progression assessment.”
BackgroundLuo, Y., Feng, J., Xu, M., Zhou, J., Min, J. K., & Xiong, G. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318776 · Cited by 3
“According to [8], medical image registration algorithms are composed of three key components: deformation model, matching criterion and optimization method.”
BackgroundMethodMedical image registration using extremal region based interest points
Kashyap, M., & Bhattacharya, M. (2015) · IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS) · doi:10.1109/cgvis.2015.7449929 · Cited by 3
“As defined in [1] Image registration is “Establishing spatial correspondences between different image acquisitions”.”
BackgroundVessel-based fast deformable registration with minimal strain energy
Huang, X., Jing Ren, A., Abdalbari, A., & Green, M. (2016) · Biomedical Engineering Letters · doi:10.1007/s13534-016-0213-7 · Cited by 3
“Deformable registration techniques can be used to register the original images with follow-up images therefore providing more accurate information for diagnosis [1-3].”
MethodNonlocal Joint Segmentation Registration Model
Ozeré, S., & Guyader, C. L. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-18461-6_28 · Cited by 3
AUTOMATIC MRF-BASED REGISTRATION OF HIGH RESOLUTION SATELLITE VIDEO DATA
Platias, C., Vakalopoulou, M., & Karantzalos, K. G. (2016) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iii-1-121-2016 · Cited by 3
“The problem of image registration has been heavily studied and numerous approaches have been proposed [Zitova and Flusser, 2003, Sotiras et al., 2013].”
MethodSemantic Parsing of Brain MR Images
Ledig, C., & Rueckert, D. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-802581-9.00014-7 · Cited by 3
“A comprehensive overview and evaluation of established registration approaches can be found in Zitov´a and Flusser (2003); Sotiras et al. (2013) and Klein et al. (2009).”
BackgroundVisual system for punched card reading in textile industry
Sharif, A., & Hafez, A. H. A. (2017) · Automatika · doi:10.1080/00051144.2018.1456097 · Cited by 3
“For example, works in [17–19] addressed the problem of image registration and presented a nice review of it.”
BackgroundA Novel Hierarchical Template Matching Model for Cardiac Motion Estimation
Bhalodiya, J. M., Palit, A., Tiwari, M. K., Prasad, S. K., Bhudia, S. K., Arvanitis, T. N., & Williams, M. A. (2018) · Scientific Reports · doi:10.1038/s41598-018-22543-y · Cited by 3
Enhancement of Bone Metastasis from CT Images Based on Salient Region Feature Registration
Sato, S., Lu, H., Kim, H. S., Murakami, S., Ueno, M., Terasawa, T., & Aoki, T. (2018) · Cited by 3
Efficient combined SSIM- and landmark-driven image registration in a variational framework
Larrey-Ruiz, J., Morales‐Sánchez, J., & Larrey-Ruiz, L. (2019) · Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería · doi:10.23967/j.rimni.2019.03.004 · Cited by 3
Optimal Transport for Diffeomorphic Registration
Feydy, J., Charlier, B., Vialard, F., & Gabriel Peyré, A. (2017) · HAL · doi:10.48550/arxiv.1706.05218 · Cited by 3
“…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.”
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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