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Abstract #3196

Quality Assessment of Pediatric Cortical Surfaces with Spherical Transformer

Jiale Cheng1,2, Xin Zhang1, Fenqiang Zhao2, Zhengwang Wu2, Ya Wang2, Ying Huang2, Weili Lin2, Li Wang2, and Gang Li2
1School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China, 2Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States


Quality assessment of cortical surfaces, which is a crucial step and prerequisite in surface-based large-scale neuroimaging studies, aims to identify the low-quality surfaces and exclude them in the subsequent analysis. Convolutional neural network-based methods have achieved great success in image quality assessment, but they are inherently inapplicable for objects presented in non-Euclidean spaces, such as the brain cortical surfaces. To this end, we propose a transformer-based network, which describes the local information based on feature correspondences among vertices, thus enabling itself to be applied directly onto a spherical manifold. Extensive experiments on 1,860 infant cortical surfaces validated its superior performance.

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