Reducing Variability in Anatomical Definitions Over Time Using Longitudinal Diffeomorphic Mapping

  • Daniel J. Tward
  • Chelsea S. Sicat
  • Timothy Brown
  • Arnold Bakker
  • Michael I. Miller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10126)

Abstract

We address the challenge of variability in the definition of anatomical structures over time in a single subject, using a template-based diffeomorphic mapping algorithm to filter out inconsistencies. Shape changes are parametrized through 2D surfaces, while data attachment is specified through dense 3D images. The mapping uses two geodesic trajectories through diffeomorphism space: template to baseline, and baseline through the timeseries. We apply this algorithm to a study of atrophy in the entorhinal and surrounding cortex in patients with mild cognitive impairment, characterized by rate of change of log-volume. We compare the uncertainty in atrophy rate measured from manual segmentations, to that computed with segmentations filtered using our longitudinal method, and to that computed from FreeSurfer. Our method correlates well with manual (correlation coefficient 0.9881, and results in significantly less variability than manual (p 8.86e-05) and FreeSurfer (p 1.03e-04).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Daniel J. Tward
    • 1
  • Chelsea S. Sicat
    • 1
  • Timothy Brown
    • 1
  • Arnold Bakker
    • 2
  • Michael I. Miller
    • 1
  1. 1.Center for Imaging ScienceJohns Hopkins UniversityBaltimoreUSA
  2. 2.Department of Psychiatry and Behavioural SciencesJohns Hopkins UniversityBaltimoreUSA

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