Symmetric Atlasing and Model Based Segmentation: An Application to the Hippocampus in Older Adults

  • Günther Grabner
  • Andrew L. Janke
  • Marc M. Budge
  • David Smith
  • Jens Pruessner
  • D. Louis Collins
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


In model-based segmentation, automated region identification is achieved via registration of novel data to a pre-determined model. The desired structure is typically generated via manual tracing within this model. When model-based segmentation is applied to human cortical data, problems arise if left-right comparisons are desired. The asymmetry of the human cortex requires that both left and right models of a structure be composed in order to effectively segment the desired structures. Paradoxically, defining a model in both hemi-spheres carries a likelihood of introducing bias to one of the structures. This paper describes a novel technique for creating a symmetric average model in which both hemispheres are equally represented and thus left-right comparison is possible. This work is an extension of that proposed by Guimond et al [1]. Hippocampal segmentation is used as a test-case in a cohort of 118 normal eld-erly subjects and results are compared with expert manual tracing.


Registration Result Standard Deviation Image Individual Patient Anatomy Computerize Brain Atlas Average Scaling Factor 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Günther Grabner
    • 1
    • 2
  • Andrew L. Janke
    • 1
  • Marc M. Budge
    • 2
  • David Smith
    • 3
  • Jens Pruessner
    • 1
  • D. Louis Collins
    • 1
  1. 1.McConnell Brain Imaging CentreMontreal Neurological InstituteCanada
  2. 2.The Australian National UniversityCanberraAustralia
  3. 3.Oxford UniversityUK

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