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Accurate Whole-Brain Segmentation for Alzheimer’s Disease Combining an Adaptive Statistical Atlas and Multi-atlas

  • Zhennan Yan
  • Shaoting Zhang
  • Xiaofeng Liu
  • Dimitris N. Metaxas
  • Albert MontilloEmail author
  • The Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8331)

Abstract

Accurate segmentation of whole brain MR images including the cortex, white matter and subcortical structures is challenging due to inter-subject variability and the complex geometry of brain anatomy. However a precise solution would enable accurate, objective measurement of structure volumes for disease quantification. Our contribution is three-fold. First we construct an adaptive statistical atlas that combines structure specific relaxation and spatially varying adaptivity. Second we integrate an isotropic pairwise class-specific MRF model of label connectivity. Together these permit precise control over adaptivity, allowing many structures to be segmented simultaneously with superior accuracy. Third, we develop a framework combining the improved adaptive statistical atlas with a multi-atlas method which achieves simultaneous accurate segmentation of the cortex, ventricles, and sub-cortical structures in severely diseased brains, a feat not attained in [18]. We test the proposed method on 46 brains including 28 diseased brain with Alzheimer’s and 18 healthy brains. Our proposed method yields higher accuracy than state-of-the-art approaches on both healthy and diseased brains.

Keywords

Brain segmentation Alzheimer’s Adaptive atlas Multi-atlas MRF 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Zhennan Yan
    • 1
  • Shaoting Zhang
    • 1
  • Xiaofeng Liu
    • 2
  • Dimitris N. Metaxas
    • 1
  • Albert Montillo
    • 2
    Email author
  • The Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing
    • 3
  1. 1.CBIMRutgers, The State University of New JerseyPiscatawayUSA
  2. 2.GE Global ResearchNiskayunaUSA
  3. 3.Commonwealth Scientific and Industrial Research OrganisationBrisbaneAustralia

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