Diffeomorphic Iterative Centroid Methods for Template Estimation on Large Datasets

  • Claire Cury
  • Joan Alexis Glaunès
  • Olivier Colliot
Chapter
Part of the Signals and Communication Technology book series (SCT)

Abstract

A common approach for analysis of anatomical variability relies on the estimation of a template representative of the population. The Large Deformation Diffeomorphic Metric Mapping is an attractive framework for that purpose. However, template estimation using LDDMM is computationally expensive, which is a limitation for the study of large datasets. This chapter presents an iterative method which quickly provides a centroid of the population in the shape space. This centroid can be used as a rough template estimate or as initialization of a template estimation method. The approach is evaluated on datasets of real and synthetic hippocampi segmented from brain MRI. The results show that the centroid is correctly centered within the population and is stable for different orderings of subjects. When used as an initialization, the approach allows to substantially reduce the computation time of template estimation.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Claire Cury
    • 1
    • 2
    • 3
    • 4
    • 5
  • Joan Alexis Glaunès
    • 6
  • Olivier Colliot
    • 1
    • 2
    • 3
    • 4
    • 5
  1. 1.UPMC Univ Paris 06, UM 75Sorbonne UniversitésParisFrance
  2. 2.UMR 7225CNRSParisFrance
  3. 3.U1127InsermParisFrance
  4. 4.ICM, Institut du Cerveau et de la Moëlle épinièreParisFrance
  5. 5.Aramis project-teamInria Paris-RocquencourtParisFrance
  6. 6.MAP5Université Paris DescartesSorbonne Paris CitéFrance

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