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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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Mutual Information Neural Estimation for Unsupervised Multi-Modal Registration of Brain Images
Snaauw, G., Sasdelli, M., Maicas, G., Lau, S., Verjans, J. W. H., Jenkinson, M., & Carneiro, G. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871220 · Cited by 14
“function, such as mutual information (MI) [7], which is one of the most studied and successful similarity metrics in multimodal iterative registration approaches [1].”
BackgroundMethod3D segmentation of the tongue in MRI: a minimally interactive model-based approach
Harandi, N. M., Rafeef Abugharbieh, A., & Fels, S. (2014) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2013.864958 · Cited by 14
“Keywords: 3D interactive segmentation; model-based registration; MRI; tongue; shape matching”
MethodWodziński, M., Skalski, A., Ciepiela, I., Kuszewski, T., Kędzierawski, P., & Gajda, J. (2018) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aaa4b1 · Cited by 14
“The majority of image registration research uses random, smooth displacements without considering the deformation characteristics (Wang et al 2005, Vercauteren et al 2009, Rohlfing 2012, Sotiras et al 2013).”
BackgroundQuicksilver: Fast Predictive Image Registration - a Deep Learning ApproachPreprint
Yang, X., Kwitt, R., Styner, M. A., & Niethammer, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.10908 · Cited by 14
“x). Here, ddenotes the spatial dimension and xis the spatial coordinate of the xed target image T.Due to the importance of image registration, a large number of dierent approaches have been proposed [1,37,38,39]. Typically, these approaches are formulated as optimization problems, where one seeks to minimize an energy…”
BackgroundDong, C., Chen, Y.-W., Seki, T., Inoguchi, R., Lin, C.-L., & Han, X. (2015) · Comput. Medical Imaging Graph. · doi:10.1016/j.compmedimag.2015.08.003 · Cited by 11
Hierarchical Sparse Representation for Robust Image Registration
Li, Y., Chen, C., Yang, F., & Huang, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2748125 · Cited by 13
“IMAGE registration is a fundamental task in image processing and computer vision [1], [2], [3].”
BackgroundMethodDeep Group-Wise Variational Diffeomorphic Image Registration
van der Ouderaa, T. F. A., Išgum, I., Veldhuis, W. B., & de Vos, B. D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-62469-9_14 · Cited by 13
Deep Learning Based Registration Using Spatial Gradients and Noisy Segmentation Labels
Estienne, T., Vakalopoulou, M., Battistella, E., Carré, A., Henry, T., Lerousseau, M., Robert, C., Nikos Paragios, A., & Deutsch, É. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-71827-5_11 · Cited by 13
“Traditionally, different types of formulations and approaches had been proposed in the last years [15] to address the problem.”
BackgroundMRI and CT Fusion in Stereotactic Electroencephalography: A Literature Review
Hinestroza, J. P., Mazo, C., Trujillo, M., & Herrera, A. (2021) · Applied Sciences · doi:10.3390/app11125524 · Cited by 13
“Non-rigid methods are sub-classified into two main categories based on the deformation model: transformations derived from physical models and transformation derived from interpolation and approximation models [33].”
MethodTowards an Architecture of a Multi-purpose, User-Extendable Reference Human Brain Atlas
Nowinski, W. L. (2021) · Neuroinformatics · doi:10.1007/s12021-021-09555-2 · Cited by 13
“Numerous methods are suitable for image warping as reviewed and compared by Ardekani et al. (2005), Klein et al. (2009), Sotiras et al. (2013), Ou et al. (2014), Viergever et al. (2016).”
MethodRecursive Deformable Image Registration Network with Mutual Attention
Zheng, J.-Q., Wang, Z., Huang, B., Vincent, T. L., Lim, N. H., & Papież, B. W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-12053-4_6 · Cited by 13
“Deformable image registration (DIR) is an essential computer vision task which has been widely studied [18].”
Background3D Visualization, Skeletonization and Branching Analysis of Blood Vessels in Angiogenesis
Ramakrishnan, V., Schönmehl, R., Artinger, A., Winter, L., Böck, H., Schreml, S., Gürtler, F., Daza, J., Schmitt, V. H., Mamilos, A., et al. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24097714 · Cited by 13
“For future research, the use of Generative Adversarial Networks (GANs) [24] could be investigated for even better registration results [25].”
MethodRodriguez, M. P., & Nygren, A. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2364959 · Cited by 13
“A nonrigid approach [33] should be explored”
Alam, F., Rahman, S. U., Khusro, S., Ullah, S., & Khalil, A. (2016) · Journal of Medical Imaging and Radiation Sciences · doi:10.1016/j.jmir.2015.12.081 · Cited by 14
A framework for scalable biophysics-based image analysis
Gholami, A., Mang, A., Scheufele, K., Davatzikos, C. A., Mehl, M., & Biros, G. (2017) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3126908.3126930 · Cited by 13
“For a detailed review on image registration algorithms see [47, 53, 59].”
BackgroundSelf-citationSegmentation method for medical image based on improved GrabCut
Lu, Y., Jiang, J., Qi, M., Zhan, S., & Yang, J. (2017) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22242 · Cited by 13
Xie, J.-C., Pun, C., Pan, Z., Gao, H., & Wang, B. (2018) · Neural Processing Letters · doi:10.1007/s11063-018-9808-6 · Cited by 13
“Image registration plays an important role in medical image analysis [1, 2].”
A lattice Boltzmann method applied to the fluid image registration
Alahyane, M., Hakim, A., Laghrib, A., & Raghay, S. (2019) · Applied Mathematics and Computation · doi:10.1016/j.amc.2018.12.051 · Cited by 13
Patch-based lung ventilation estimation using multi-layer supervoxels
Szmul, A., Matin, T., Gleeson, F. V., Schnabel, J. A., Grau, V., & Papież, B. W. (2019) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2019.04.002 · Cited by 13
Nonrigid registration for tracking incompressible soft tissues with sliding motion
Ai, D., Liu, D., Wang, Y., Fu, T., Huang, Y., Jiang, Y., Song, H., Wang, Y., Liang, P., & Yang, J. (2019) · Medical Physics · doi:10.1002/mp.13694 · Cited by 13
Learning-Based Affine Registration of Histological Images
Wodziński, M., & Müller, H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50120-4_2 · Cited by 13
“Medical image registration can be divided into classical algorithms, involving an iterative optimization for each image pair [11] or learning-based algorithms where the transformations are being learned and then the registration is performed during the inference [12].”
BackgroundMesin, L., Mokabberi, F., & Carlino, C. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3016886 · Cited by 13
“1) Classical Demons: Demons method is a non-parametric, non-rigid image registration based on a diffusion process [44].”
MethodAdaptive Physics-Based Non-Rigid Registration for Immersive Image-Guided Neuronavigation Systems
Drakopoulos, F., Tsolakis, C., Angelopoulos, A., Liu, Y., Yao, C., Kavazidi, K. R., Foroglou, N., Fedorov, A., Frisken, S. F., Kikinis, R., et al. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2020.613608 · Cited by 13
Principal component analysis is limited to low-resolution analysis in cryoEM
Sorzano, C. Ó. S., & Carazo, J. M. (2021) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798321002291 · Cited by 13
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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