Fast Predictive Simple Geodesic Regression

  • Zhipeng Ding
  • Greg Fleishman
  • Xiao Yang
  • Paul Thompson
  • Roland Kwitt
  • Marc Niethammer
  • The Alzheimer’s Disease Neuroimaging Initiative
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10553)


Analyzing large-scale imaging studies with thousands of images is computationally expensive. To assess localized morphological differences, deformable image registration is a key tool. However, as registrations are costly to compute, large-scale studies frequently require large compute clusters. This paper explores a fast predictive approximation to image registration. In particular, it uses these fast registrations to approximate a simplified geodesic regression model to capture longitudinal brain changes. The resulting approach is orders of magnitude faster than the optimization-based regression approach and hence facilitates large-scale analysis on a single graphics processing unit. We show results on 2D and 3D brain magnetic resonance images from OASIS and ADNI.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Zhipeng Ding
    • 1
  • Greg Fleishman
    • 3
    • 4
  • Xiao Yang
    • 1
  • Paul Thompson
    • 3
  • Roland Kwitt
    • 5
  • Marc Niethammer
    • 1
    • 2
  • The Alzheimer’s Disease Neuroimaging Initiative
  1. 1.Department of Computer ScienceUNC Chapel HillChapel HillUSA
  2. 2.Biomedical Research Imaging CenterUNC Chapel HillChapel HillUSA
  3. 3.Imaging Genetics CenterUSCLos AngelesUSA
  4. 4.Department of RadiologyUniversity of PennsylvaniaPhiladelphiaUSA
  5. 5.Department of Computer ScienceUniversity of SalzburgSalzburgAustria

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