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Automated Segmentation of the Cerebellar Lobules Using Boundary Specific Classification and Evolution

  • John A. Bogovic
  • Pierre-Louis Bazin
  • Sarah H. Ying
  • Jerry L. Prince
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7917)

Abstract

The cerebellum is instrumental in coordinating many vital functions ranging from speech and balance to eye movement. The effect of cerebellar pathology on these functions is frequently examined using volumetric studies that depend on consistent and accurate delineation, however, no existing automated methods adequately delineate the cerebellar lobules. In this work, we describe a method we call the Automatic Classification of Cerebellar Lobules Algorithm using Implicit Multi-boundary evolution (ACCLAIM). A multiple object geometric deformable model (MGDM) enables each boundary surface of each individual lobule to be evolved under different level set speeds. An important innovation described in this work is that the speed for each lobule boundary is derived from a classifier trained specifically to identify that boundary. We compared our method to segmentations obtained using the atlas-based and multi-atlas fusion techniques, and demonstrate ACCLAIM’s superior performance.

Keywords

Random Forest Active Contour Cerebellar Atrophy Cerebellar Lobule Neighboring Object 
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 2013

Authors and Affiliations

  • John A. Bogovic
    • 1
  • Pierre-Louis Bazin
    • 2
  • Sarah H. Ying
    • 3
  • Jerry L. Prince
    • 1
  1. 1.Department of Electrical and Computer EngineeringJohns Hopkins UniversityBaltimoreUSA
  2. 2.Department of NeurophysicsMax Plank Institute for Human Cognitive and Brain SciencesLeipzigGermany
  3. 3.Departments of Radiology, Neurology and OpthamologyJohns Hopkins School of MedicineBaltimoreUSA

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