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Cited by 2,227Medical Image Segmentation TechniquesAdvanced MRI Techniques and ApplicationsFunctional Brain Connectivity Studies

Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration

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Klein, Arno, Andersson, Jesper, Ardekani, Babak A., Ashburner, John, Avants, Brian, Chiang, Ming-Chang, Christensen, Gary E., Collins, D. Louis +11 more·2009·NeuroImage
DOI 10.1016/j.neuroimage.2008.12.037PMC PMC2747506
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Abstract

All fields of neuroscience that employ brain imaging need to communicate their results with reference to anatomical regions. In particular, comparative morphometry and group analysis of functional and physiological data require coregistration of brains to establish correspondences across brain structures. It is well established that linear registration of one brain to another is inadequate for aligning brain structures, so numerous algorithms have emerged to nonlinearly register brains to one another. This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted. Fourteen algorithms from laboratories around the world are evaluated using 8 different error measures. More than 45,000 registrations between 80 manually labeled brains were performed by algorithms including: AIR, ANIMAL, ART, Diffeomorphic Demons, FNIRT, IRTK, JRD-fluid, ROMEO, SICLE, SyN, and four different SPM5 algorithms ("SPM2-type" and regular Normalization, Unified Segmentation, and the DARTEL Toolbox). All of these registrations were preceded by linear registration between the same image pairs using FLIRT. One of the most significant findings of this study is that the relative performances of the registration methods under comparison appear to be little affected by the choice of subject population, labeling protocol, and type of overlap measure. This is important because it suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols. Furthermore, we ranked the 14 methods according to three completely independent analyses (permutation tests, one-way ANOVA tests, and indifference-zone ranking) and derived three almost identical top rankings of the methods. ART, SyN, IRTK, and SPM's DARTEL Toolbox gave the best results according to overlap and distance measures, with ART and SyN delivering the most consistently high accuracy across subjects and label sets. Updates will be published on the http://www.mindboggle.info/papers/ website.

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  • More aggressive deformations may result in higher atlas-to-target regional overlaps, which is not ideal for registration but preferable for atlas-based segmentation.

    “In comparative survey papers (Ou et al., 2014; Klein et al., 2009) and in many other studies, it was reported that more aggressive deformations may result in higher atlas-to-target regional overlaps, which is not ideal for registration but preferable for atlas-based segmentation.”

    From MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection (2016) · cites this paper as Klein et al., 2009

    The Discussion (page unavailable) reports a modest correlation between a deformation’s degrees of freedom and registration accuracy, consistent with more flexible methods achieving better measured results. The Caveats section (page unavailable) warns that overlap measures can miss misregistration and folding. The paper does not establish that aggressive deformations are preferable for atlas-based segmentation but not ideal for registration. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

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Authors
Klein, A., Andersson, J., Ardekani, B. A., Ashburner, J., Avants, B., Chiang, M.-C., Christensen, G. E., Collins, D. L., Gee, J., Hellier, P., Song, J. H., Jenkinson, M., Lepage, C., Rueckert, D., Thompson, P., Vercauteren, T., Woods, R. P., Mann, J. J., & Parsey, R. V.
Affiliations
  • New York State Psychiatric Institute (US)
  • John Radcliffe Hospital (GB)
  • Nathan Kline Institute for Psychiatric Research (US)
  • Wellcome Centre for Human Neuroimaging (GB)
  • University of Pennsylvania (US)
  • University of California, Los Angeles (US)
  • University of Iowa (US)
  • Montreal Neurological Institute and Hospital (CA)
  • Imperial College London (GB)
  • Institut national de recherche en sciences et technologies du numérique (FR)
Corresponding author
Klein, Arno
ORCID
Venue
NeuroImage
Publisher
Elsevier BV
Published
2009-07
Volume
46
Issue
3
Pages
786-802
ISSN
1053-8119 (ISSN-L)
DOI
10.1016/j.neuroimage.2008.12.037
PMC
PMC2747506
OpenAlex
W2102099319
Cited by
2,227
References
2
Citations by year
2026: 63 · 2025: 85 · 2024: 79 · 2023: 96 · 2022: 123 · 2021: 141 · 2020: 168 · 2019: 127 · 2018: 153 · 2017: 163 · 2016: 138 · 2015: 167 · 2014: 179 · 2013: 144 · 2012: 147
FWCI
147.53
Type
journal article
Language
en
Open access
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • NeuroImage (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • HAL (submittedVersion, other-oa)
  • UCL Discovery (University College London) (submittedVersion)
Funders
  • Wellcome Trust
  • Biotechnology and Biological Sciences Research Council (BB/C519938/1)
  • National Institute of Mental Health (R01MH040695, R01MH084029, P50MH062185)
  • National Institute of Biomedical Imaging and Bioengineering (EB 004126, R21EB004126, R33EB004126, R03EB008201)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords
brain MRI registration, algorithm comparison, anatomical labeling, DARTEL, overlap measure, brain morphometry, group analysis
MeSH terms
Adult, Algorithms, Artificial Intelligence, Brain, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Pattern Recognition, Automated, Sensitivity and Specificity, Subtraction Technique, Reproducibility of Results, Nonlinear Dynamics

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