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Neuroinformatics

, Volume 13, Issue 3, pp 367–381 | Cite as

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

  • Chuyang YeEmail author
  • Zhen Yang
  • Sarah H. Ying
  • Jerry L. Prince
Original Article

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 with spinocerebellar ataxia type 6 (SCA6). It was then used to evaluate group differences in the peduncles in a population of 32 controls and 11 SCA6 patients. In the SCA6 group, we have observed significant decreases in the volumes of the dSCP and the ICPs and significant increases in the mean diffusivity in the noncrossing SCPs, the MCP, and the ICPs. These results are consistent with a degeneration of the cerebellar peduncles in SCA6 patients.

Keywords

Cerebellar peduncles Random forest classifier Multi-object geometric deformable model Spinocerebellar ataxia type 6 

Notes

Acknowledgments

This work is supported by NIH/NINDS 5R01NS056307-08 and the China Scholarship Council.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Chuyang Ye
    • 1
    Email author
  • Zhen Yang
    • 1
  • Sarah H. Ying
    • 2
  • Jerry L. Prince
    • 1
  1. 1.Department of Electrical and Computer EngineeringJohns Hopkins UniversityBaltimoreUSA
  2. 2.Departments of Radiology, Neurology, and OphthalmologyJohns Hopkins University School of MedicineBaltimoreUSA

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