Publication

Robust estimation of group-wise cortical correspondence with an application to macaque and human neuroimaging studies

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Last modified
  • 02/20/2025
Type of Material
Authors
    Ilwoo Lyu, University of North Carolina at Chapel HillSun H. Kim, University of North Carolina at Chapel HillJoon-Kyung Seong, Korea UniversitySang W. Yoo, Samsung ElectronicsAlan Evans, McGill UniversityYundi Shi, University of North Carolina at Chapel HillMaria Sanchez, Emory UniversityMarc Niethammer, University of North Carolina at Chapel HillMartin A. Styner, University of North Carolina at Chapel Hill
Language
  • English
Date
  • 2015-06-11
Publisher
  • Frontiers Media
Publication Version
Copyright Statement
  • © 2015 Lyu, Kim, Seong, Yoo, Evans, Shi, Sanchez, Niethammer and Styner.
License
Final Published Version (URL)
Title of Journal or Parent Work
ISSN
  • 1662-4548
Volume
  • 9
Grant/Funding Information
  • This work was funded by the National Institutes of Health (NIH) under Grant Nos. R01 MH091645, U54 EB005149, P50 MH078105- 01A2S1, P50 MH078105, P50 MH100029, P30 HD003110, U54 HD079124, R01 HD055741.
Abstract
  • We present a novel group-wise registration method for cortical correspondence for local cortical thickness analysis in human and non-human primate neuroimaging studies. The proposed method is based on our earlier template based registration that estimates a continuous, smooth deformation field via sulcal curve-constrained registration employing spherical harmonic decomposition of the deformation field. This pairwise registration though results in a well-known template selection bias, which we aim to overcome here via a group-wise approach. We propose the use of an unbiased ensemble entropy minimization following the use of the pairwise registration as an initialization. An individual deformation field is then iteratively updated onto the unbiased average. For the optimization, we use metrics specific for cortical correspondence though all of these are straightforwardly extendable to the generic setting: The first focused on optimizing the correspondence of automatically extracted sulcal landmarks and the second on that of sulcal depth property maps. We further propose a robust entropy metric and a hierarchical optimization by employing spherical harmonic basis orthogonality. We also provide the detailed methodological description of both our earlier work and the proposed method with a set of experiments on a population of human and non-human primate subjects. In the experiment, we have shown that our method achieves superior results on consistency through quantitative and visual comparisons as compared to the existing methods.
Author Notes
  • Correspondence: Ilwoo Lyu, Department of Computer Science, University of North Carolina at Chapel Hill, Sitterson Hall, Campus Box 3175, Chapel Hill, NC27599-3175, USA; ilwoolyu@cs.unc.edu.
Keywords
Research Categories
  • Biology, Neuroscience

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