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Proceedings Paper

A toolbox to visually explore cerebellar shape changes in cerebellar disease and dysfunction
Author(s): S. Mazdak Abulnaga; Zhen Yang; Aaron Carass; Kalyani Kansal; Bruno M. Jedynak; Chiadi U. Onyike; Sarah H. Ying; Jerry L. Prince
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Paper Abstract

The cerebellum plays an important role in motor control and is also involved in cognitive processes. Cerebellar function is specialized by location, although the exact topographic functional relationship is not fully understood. The spinocerebellar ataxias are a group of neurodegenerative diseases that cause regional atrophy in the cerebellum, yielding distinct motor and cognitive problems. The ability to study the region-specific atrophy patterns can provide insight into the problem of relating cerebellar function to location. In an effort to study these structural change patterns, we developed a toolbox in MATLAB to provide researchers a unique way to visually explore the correlation between cerebellar lobule shape changes and function loss, with a rich set of visualization and analysis modules. In this paper, we outline the functions and highlight the utility of the toolbox. The toolbox takes as input landmark shape representations of subjects’ cerebellar substructures. A principal component analysis is used for dimension reduction. Following this, a linear discriminant analysis and a regression analysis can be performed to find the discriminant direction associated with a specific disease type, or the regression line of a specific functional measure can be generated. The characteristic structural change pattern of a disease type or of a functional score is visualized by sampling points on the discriminant or regression line. The sampled points are used to reconstruct synthetic cerebellar lobule shapes. We showed a few case studies highlighting the utility of the toolbox and we compare the analysis results with the literature.

Paper Details

Date Published: 24 March 2016
PDF: 10 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97852P (24 March 2016); doi: 10.1117/12.2216584
Show Author Affiliations
S. Mazdak Abulnaga, The Univ. of British Columbia (Canada)
Zhen Yang, Johns Hopkins Univ. (United States)
Aaron Carass, Johns Hopkins Univ. (United States)
Kalyani Kansal, The Johns Hopkins Univ. School of Medicine (United States)
Bruno M. Jedynak, Portland State Univ. (United States)
Chiadi U. Onyike, The Johns Hopkins Univ. School of Medicine (United States)
Sarah H. Ying, The Johns Hopkins Univ. School of Medicine (United States)
Jerry L. Prince, Johns Hopkins Univ. (United States)
The Johns Hopkins Univ. School of Medicine (United States)


Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato III, Editor(s)

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