Deformable medical image registration: a survey

Sotiras, A.; Davatzikos, C.; Paragios, N.

doi:10.1109/tmi.2013.2265603

Cited by 1,587Medical Image Segmentation TechniquesAdvanced Radiotherapy TechniquesMedical Imaging and Analysis

Deformable medical image registration: a survey

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Sotiras, A., Davatzikos, C., Paragios, N.·2013·IEEE Transactions on Medical Imaging

DOI 10.1109/tmi.2013.2265603PMC PMC3745275

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Abstract

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 is a core component of all multi-atlas segmentation (MAS) methods, and the quality of individual registrations has a very high impact on the accuracy of the final segmentation.
“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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“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…”
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Authors: Sotiras, A., Davatzikos, C., & Paragios, N.
Affiliations: University of Pennsylvania (US)
ORCID: Sotiras, A.
Davatzikos, C.
Venue: IEEE Transactions on Medical Imaging
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2013-07
Volume: 32
Issue: 7
Pages: 1153-1190
ISSN: 0278-0062 (ISSN-L)
1558-254X
DOI: 10.1109/tmi.2013.2265603
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 · 2012: 1
FWCI: 18.05
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
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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Deformable medical image registration: a survey

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