Automated segmentation of the cerebellar lobules using boundary specific classification and evolution

Authors:
John A. Bogovic;Pierre-Louis Bazin;Sarah H. Ying;Jerry L. Prince
Affiliations:
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD;Department of Neurophysics, Max Plank Institute for Human Cognitive and Brain Sciences, Leipzig, Germany;Departments of Radiology, Neurology and Opthamology, Johns Hopkins School of Medicine, Baltimore, MD;Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD
Venue:
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
Year:
2013

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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.