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Geometric Convolutional Neural Network for Analyzing Surface-Based Neuroimaging Data.

Authors
 Si-Baek Seong  ;  Chongwon Pae  ;  Hae-Jeong Park 
Citation
 Frontiers in Neuroinformatics, Vol.12 : 42, 2018 
Journal Title
 Frontiers in Neuroinformatics 
Issue Date
2018
Keywords
Machine learning ; cortical thickness ; geometric convolutional neural network ; neuroimage ; sex differences ; surface-based analysis
Abstract
In machine learning, one of the most popular deep learning methods is the convolutional neural network (CNN), which utilizes shared local filters and hierarchical information processing analogous to the brain's visual system. Despite its popularity in recognizing two-dimensional (2D) images, the conventional CNN is not directly applicable to semi-regular geometric mesh surfaces, on which the cerebral cortex is often represented. In order to apply the CNN to surface-based brain research, we propose a geometric CNN (gCNN) that deals with data representation on a mesh surface and renders pattern recognition in a multi-shell mesh structure. To make it compatible with the conventional CNN toolbox, the gCNN includes data sampling over the surface, and a data reshaping method for the convolution and pooling layers. We evaluated the performance of the gCNN in sex classification using cortical thickness maps of both hemispheres from the Human Connectome Project (HCP). The classification accuracy of the gCNN was significantly higher than those of a support vector machine (SVM) and a 2D CNN for thickness maps generated by a map projection. The gCNN also demonstrated position invariance of local features, which rendered reuse of its pre-trained model for applications other than that for which the model was trained without significant distortion in the final outcome. The superior performance of the gCNN is attributable to CNN properties stemming from its brain-like architecture, and its surface-based representation of cortical information. The gCNN provides much-needed access to surface-based machine learning, which can be used in both scientific investigations and clinical applications.
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DOI
10.3389/fninf.2018.00042
Appears in Collections:
1. Journal Papers (연구논문) > 1. College of Medicine (의과대학) > Dept. of Nuclear Medicine (핵의학교실)
Yonsei Authors
박해정(Park, Hae Jeong) ORCID logo https://orcid.org/0000-0002-4633-0756
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URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/165553
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