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Proceedings Paper

Cerebellum parcellation with convolutional neural networks
Author(s): Shuo Han; Yufan He; Aaron Carass; Sarah H. Ying; Jerry L. Prince
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Paper Abstract

To better understand cerebellum-related diseases and functional mapping of the cerebellum, quantitative measurements of cerebellar regions in magnetic resonance (MR) images have been studied in both clinical and neurological studies. Such studies have revealed that different spinocerebellar ataxia (SCA) subtypes have different patterns of cerebellar atrophy and that atrophy of different cerebellar regions is correlated with specific functional losses. Previous methods to automatically parcellate the cerebellum, that is, to identify its sub-regions, have been largely based on multi-atlas segmentation. Recently, deep convolutional neural network (CNN) algorithms have been shown to have high speed and accuracy in cerebral sub-cortical structure segmentation from MR images. In this work, two three-dimensional CNNs were used to parcellate the cerebellum into 28 regions. First, a locating network was used to predict a bounding box around the cerebellum. Second, a parcellating network was used to parcellate the cerebellum using the entire region within the bounding box. A leave-one-out cross validation of fifteen manually delineated images was performed. Compared with a previously reported state-ofthe-art algorithm, the proposed algorithm shows superior Dice coefficients. The proposed algorithm was further applied to three MR images of a healthy subject and subjects with SCA6 and SCA8, respectively. A Singularity container of this algorithm is publicly available.

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109490K (15 March 2019); doi: 10.1117/12.2512119
Show Author Affiliations
Shuo Han, The Johns Hopkins Univ. School of Medicine (United States)
National Institutes of Health (United States)
Yufan He, Johns Hopkins Univ. (United States)
Aaron Carass, Johns Hopkins Univ. (United States)
Sarah H. Ying, The Johns Hopkins Univ. School of Medicine (United States)
Jerry L. Prince, Johns Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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