Segmentation of the Cerebellar Peduncles Using a Random Forest Classifier and a Multi-object Geometric Deformable Model: Application to Spinocerebellar Ataxia Type 6

Neuroinformatics
Chuyang YeJerry L Prince

Abstract

The cerebellar peduncles, comprising the superior cerebellar peduncles (SCPs), the middle cerebellar peduncle (MCP), and the inferior cerebellar peduncles (ICPs), are white matter tracts that connect the cerebellum to other parts of the central nervous system. Methods for automatic segmentation and quantification of the cerebellar peduncles are needed for objectively and efficiently studying their structure and function. Diffusion tensor imaging (DTI) provides key information to support this goal, but it remains challenging because the tensors change dramatically in the decussation of the SCPs (dSCP), the region where the SCPs cross. This paper presents an automatic method for segmenting the cerebellar peduncles, including the dSCP. The method uses volumetric segmentation concepts based on extracted DTI features. The dSCP and noncrossing portions of the peduncles are modeled as separate objects, and are initially classified using a random forest classifier together with the DTI features. To obtain geometrically correct results, a multi-object geometric deformable model is used to refine the random forest classification. The method was evaluated using a leave-one-out cross-validation on five control subjects and four patients wi...Continue Reading

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Citations

Aug 22, 2015·Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society·Chuyang YeJerry L Prince
May 23, 2016·Medical Image Analysis·Chuyang YeJerry L Prince
Sep 28, 2016·Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society·Chuyang Ye, Jerry L Prince
Jan 13, 2017·Annals of Clinical and Translational Neurology·Sub H Subramony

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