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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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Local statistical deformation models for deformable image registration
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H-SGANet: Hybrid sparse graph attention network for deformable medical image registration
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A Physics-Informed Deep Learning Deformable Medical Image Registration Method Based on Neural ODEs
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BackgroundA matrix-free approach to efficient affine-linear image registration on CPU and GPU
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Multi-Modal Unsupervised Brain Image Registration Using Edge Maps
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One Registration is Worth Two Segmentations
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“Similarly to the last techniques, interpolation using MQF is a minimization problem defined as [19, 20] Eu D NX iD1 Œu .xi ; yi / u0.xi ; yi / 2; (9) with u.x; y/ D NX iD1 ˛iR .j.x; y/ .xi ; yi /j ; "/; (10) R.r; "/ D p 1C "2r2: (11) In this work, " was empirically set to 1=2.”
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“While a large number of image registration algorithms have been proposed to estimate the transformation that best aligns two or more images (Sotiras et al., 2013), registering pre-procedural imaging with intra- or post-procedural imaging is particularly difficult due to a number of factors that may…”
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“Hence, accurate deformable registration [2] is the need of the hour.”
BackgroundQin, B., Shen, Z., Fu, Z., Zhou, Z., Lv, Y., & Bao, J. (2017) · IEEE Access · doi:10.1109/access.2017.2762901 · Cited by 8
“Deformable image registration [1], or optical flow [2] computation (referred to as monomodal deformable image regis-”
Primal and Mixed Finite Element Methods for Deformable Image Registration Problems
Barnafi, N. A., Gatica, G. N., & Hurtado, D. E. (2018) · SIAM Journal on Imaging Sciences · doi:10.1137/17m115219x · Cited by 8
“Such problem arises in a number of important applications, particularly in the field of medical imaging [39].”
BackgroundLow-Rank Based Image Analyses for Pathological MR Image Segmentation and Recovery
Lin, C., Wang, Y., Wang, T., & Ni, D. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00333 · Cited by 8
“However, in addition to tumor segmentation task, the presence of pathologies in MR images poses challenges in other image computing tasks, such as intensity-/feature-based image registration (Sotiras et al., 2013) and atlas-based segmentation of brain structures (Cabezas et al.”
BackgroundSaygılı, G. (2020) · Signal Image and Video Processing · doi:10.1007/s11760-020-01650-2 · Cited by 8
“Many accurate registration algorithms exist[1–3],andanextensiveoverviewofthesealgorithmscan be found in [4,5].”
BackgroundScale‐adaptive deep network for deformable image registration
Sang, Y., & Ruan, D. (2021) · Medical Physics · doi:10.1002/mp.14935 · Cited by 8
Improving performance of medical image alignment through super-resolution
Deng, L., Zhang, Y., Wang, J., Huang, S., & Yang, X. N. (2023) · Biomedical Engineering Letters · doi:10.1007/s13534-023-00268-w · Cited by 9
“Generally, medical image alignment networks using CNNs minimize the energy function of equation 4 by pre-training on a large dataset to obtain a generalizable alignment model [19].”
MethodImproving robustness for inter-subject medical image registration using a feature-based approach
Svärm, L., Enqvist, O., Kahl, F., & Oskarsson, M. (2015) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7163998 · Cited by 8
“Intensity-based methods consider information from the whole image, while feature-based ones rely on geometrically defined interest points, see [1] and the references therein.”
Learning-Based Shape Model Matching: Training Accurate Models with Minimal Manual Input
Lindner, C., Thomson, J., The arcOGEN Consortium, & Cootes, T. F. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_69 · Cited by 8
“We use a pragmatic, but effective, approach in which we use a small number of manually annotated points on each image to initialise a dense groupwise nonrigid registration (GNR) [10] to establish correspondences across the set of images, allowing us to propagate a dense annotation from one image to all the rest and th…”
MethodA Stochastic Quasi-Newton Method for Non-Rigid Image Registration
Qiao, Y., Sun, Z., Lelieveldt, B. P. F., & Staring, M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24571-3_36 · Cited by 8
“However, this process is often very slow because of the large number of voxels in the images and the complexity of the registration algorithms [1,2].”
BackgroundLegaz-Aparicio, Á.-G., Verdú‐Monedero, R., Larrey-Ruiz, J., Morales‐Sánchez, J., López-Mir, F., Naranjo, V., & Bernabéu, Á. (2016) · International Journal of Neural Systems · doi:10.1142/s0129065717500149 · Cited by 8
Swarm Intelligence Algorithms for Medical Image Registration: A Comparative Study
Sarvamangala, D. R., & Kulkarni, R. V. (2017) · Communications in computer and information science · doi:10.1007/978-981-10-6430-2_35 · Cited by 8
Hernández, M. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa925a · Cited by 8
“This justifies the vast literature on image registration methods with differences on transformation characterization, regularizers, image similarity metrics, optimization methods, and additional constraints (Sotiras et al 2013).”
BackgroundBricq, S., Kidane, H. L., Bojorquez, J. Z., Oudot, A., Vrigneaud, J., Brunotte, F., WALKER, P. M., Cochet, A., & Lalande, A. (2018) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-018-1797-0 · Cited by 8
Biomechanics-based graph matching for augmented CT-CBCT
Guevara, J. G., Peterlík, I., Berger, M., & Cotin, S. (2018) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-018-1755-1 · Cited by 8
“When deformable organs are considered, registration requires to define a deformation model, as well as an objective criteria which quantifies the alignment between the target and the source images [4].”
BackgroundA Novel Similarity Measure for Image Sequences
Brehmer, K., Wacker, B., & Jan Modersitzki, C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-92258-4_5 · Cited by 8
“For pairs of two images, a variety of options such as sum of squared differences (SSD), normalized gradient fields (NGF), or mutual information (MI) exists and these measures are well-understood; see e.g. [12,13,17].”
BackgroundHernandez, M. (2018) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aaf598 · Cited by 8
“In the last two decades, diffeomorphic image registration has arisen as a powerful paradigm for deformable image registration [13].”
BackgroundJoint Registration and Change Detection in Longitudinal Brain MRI
Dufresne, E., Fortun, D., Kumar, B., Kremer, S., & Noblet, V. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098626 · Cited by 8
“An overview of deformable registration methods in medical imaging can be found in [6].”
Faster dense deformable image registration by utilizing both CPU and GPU
Ekström, S., Pilia, M., Kullberg, J., Ahlström, H. K., Strand, R., & Malmberg, F. (2021) · Journal of medical imaging · doi:10.1117/1.jmi.8.1.014002 · Cited by 8
Hernandez, M. (2020) · Zaguan (University of Zaragoza Repository) · Cited by 8
Graph-Based Slice-to-Volume Deformable Registration
Ferrante, E., & Nikos Paragios, A. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1040-8 · Cited by 8
Fast Mesh-Based Medical Image RegistrationPreprint
Baghaie, A., Yu, Z., & D’Souza, R. M. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.2141 · Cited by 8
“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.”
MethodIn-silico CT lung phantom generated from finite-element mesh
Neelakantan, S., Mukherjee, T., Smith, B. J., Myers, K. J., Rizi, R. R., & Avazmohammadi, R. (2024) · SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling · doi:10.1117/12.3006973 · Cited by 7
Advanced Image Stitching Method for Dual-Sensor Inspection
Shahsavarani, S., López, F., Ibarra‐Castanedo, C., & Maldague, X. (2024) · Sensors · doi:10.3390/s24123778 · Cited by 7
Mamba? Catch The Hype Or Rethink What Really Helps for Image Registration
Jian, B., Pan, J., Ghahremani, M., Rueckert, D., Wachinger, C., & Wiestler, B. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73480-9_7 · Cited by 7
“Efficient and accurate deformable registration is fundamental in neuroimaging analysis, facilitating the understanding of human brain dynamics and the disease course assessment of various neurological disorders [36].”
BackgroundShakoorioskooie, M., Granget, E., Cocen, O. R., Hovind, J., Mannes, D. C., Kaestner, A. P., & Brambilla, L. (2024) · Applied Physics A · doi:10.1007/s00339-024-07990-x · Cited by 7
“The hierarchical approaches aim to reduce the mentioned complexities [39].”
BackgroundZhang, Z., Criscuolo, E. R., Hao, Y. W., McKeown, T., & Yang, D. (2024) · Medical Physics · doi:10.1002/mp.17507 · Cited by 7
“12,13 However, quantitatively evaluating the accuracy of DIR presents considerable challenges.”
Liu, Y., Wang, L., Ning, X., Gao, Y., & Wang, D. (2025) · iScience · doi:10.1016/j.isci.2024.111734 · Cited by 7
“In the realm of medical image registration, conventional methods have primarily focused on optimizing transformations through iterative minimization of the energy function, specifically Equation 1.”
Wang, H., Wang, Z., Ni, D., & Wang, Y. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103862 · Cited by 7
“Deformable image registration (DIR) has always been an important focus in the society of medical imaging, which is widely used for the preoperative planning, disease diagnosis and intraoperative guidance (Sotiras et al., 2013; Ferrante and Paragios, 2017; Fu et al., 2020).”
Optimization of an on‐board imaging system for extremely rapid radiation therapy
Cherry, E. M., Wu, M., Yang, H., Maxim, P. G., Loo, B. W., & Fahrig, R. (2015) · Medical Physics · doi:10.1118/1.4934377 · Cited by 7
Visualizing Shape Deformations with Variation of Geometric Spectrum
Hu, J., Hamidian, H., Zhong, Z., & Hua, J. (2016) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2598790 · Cited by 7
“The ICP method has been used in many research areas such as multi-modality image registration [35].”
MethodÜberatlas: Robust Speed-Up of Feature-Based Registration and Multi-Atlas Segmentation
Alvén, J., Norlén, A., Enqvist, O., & Kahl, F. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19665-7_8 · Cited by 7
“In principle, there are two different approaches to image registration, featurebased and intensity-based registration, see the surveys [14, 25].”
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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