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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