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Automated Sulci Identification via Intrinsic Modeling of Cortical Anatomy

  • Yonggang Shi
  • Bo Sun
  • Rongjie Lai
  • Ivo Dinov
  • Arthur W. Toga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6363)

Abstract

In this paper we propose a novel and robust system for the automated identification of major sulci on cortical surfaces. Using multiscale representation and intrinsic surface mapping, our system encodes anatomical priors in manually traced sulcal lines with an intrinsic atlas of major sulci. This allows the computation of both individual and joint likelihood of sulcal lines for their automatic identification on cortical surfaces. By modeling sulcal anatomy with intrinsic geometry, our system is invariant to pose differences and robust across populations and surface extraction methods. In our experiments, we present quantitative validations on twelve major sulci to show the excellent agreement of our results with manually traced curves. We also demonstrate the robustness of our system by successfully applying an atlas of Chinese population to identify sulci on Caucasian brains of different age groups, and surfaces extracted by three popular software tools.

Keywords

Cortical Surface Joint Likelihood Intrinsic Geometry Quantitative Validation Multiscale Representation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yonggang Shi
    • 1
  • Bo Sun
    • 2
  • Rongjie Lai
    • 3
  • Ivo Dinov
    • 1
  • Arthur W. Toga
    • 1
  1. 1.Lab of Neuro ImagingUCLA School of MedicineLos AngelesUSA
  2. 2.Shandong University School of MedicineJinan, ShandongChina
  3. 3.Department of MathematicsUCLALos AngelesUSA

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