Published in

Springer, Lecture Notes in Computer Science, p. 62-73, 2013

DOI: 10.1007/978-3-642-38868-2_6



Export citation

Search in Google Scholar

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

Book chapter published in 2013 by John A. Bogovic, Pierre-Louis Bazin ORCID, Sarah H. Ying, Jerry L. Prince
This paper is available in a repository.
This paper is available in a repository.

Full text: Download

Red circle
Preprint: archiving forbidden
Orange circle
Postprint: archiving restricted
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO


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.