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)


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).


Mild Cognitive Impairment Entorhinal Cortex Manual Segmentation Atrophy Rate Anatomical Definition 
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.



This project was supported by the National Center for Research Resources and the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health through Grant Number P41 EB015909. This work was supported by the Kavli Foundation. This work used the Extreme Science and Engineering Discovery Environment (XSEDE) [16], which is supported by National Science Foundation grant number ACI-1053575.

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.


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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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