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Proceedings Paper

Labeling of the cerebellar peduncles using a supervised Gaussian classifier with volumetric tract segmentation
Author(s): Chuyang Ye; Pierre-Louis Bazin; John A. Bogovic; Sarah H. Ying; Jerry L. Prince
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Paper Abstract

The cerebellar peduncles are white matter tracts that play an important role in the communication of the cerebellum with other regions of the brain. They can be grouped into three fiber bundles: inferior cerebellar peduncle middle cerebellar peduncle, and superior cerebellar peduncle. Their automatic segmentation on diffusion tensor images would enable a better understanding of the cerebellum and would be less time-consuming and more reproducible than manual delineation. This paper presents a method that automatically labels the three fiber bundles based on the segmentatin results from the diffusion oriented tract segmentation (DOTS) algorithm, which achieves volume segmentation of white matter tracts using a Markov random field (MRF) framework. We use the DOTS labeling result as a guide to determine the classification of fibers produced by wild bootstrap probabilistic tractography. Mean distances from each fiber to each DOTS volume label are defined and then used as features that contribute to classification. A supervised Gaussian classifier is employed to label the fibers. Manually delineated cerebellar peduncles serve as training data to determine the parameters of class probabilities for each label. Fibers are labeled ad the class that has the highest posterior probability. An outlier detection ste[ re,pves fober tracts that belong to noise of that are not modeled by DOTS. Experiments show a successful classification of the cerebellar peduncles. We have also compared results between successive scans to demonstrate the reproducibility of the proposed method.

Paper Details

Date Published: 24 February 2012
PDF: 6 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 831447 (24 February 2012); doi: 10.1117/12.910551
Show Author Affiliations
Chuyang Ye, The Johns Hopkins Univ. (United States)
Pierre-Louis Bazin, Max-Planck-Institute for Human Cognitive and Brain Sciences (Germany)
John A. Bogovic, The Johns Hopkins Univ. (United States)
Sarah H. Ying, The Johns Hopkins Univ. School of Medicine (United States)
Jerry L. Prince, The Johns Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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