Keywords: Graph Convolution Networks, Geometric Deep Learning, Cortical Parcellation
Abstract: Brain surface analysis is challenging due to the high variability of the cortical geometry. This paper presents a novel graph convolutional based approach for learning surface data directly across multiple surfaces. Current methods either rely on costly geometrical simplification processes or lack the ability to compare surface data across different domains. Our work leverages advances in spectral graph matching to align incompatible surface bases to a reference surface for direct learning of surface data. We illustrate with a cortical parcellation application the benefits of our method. We validate the algorithm over 101 manually labeled brain surfaces. The improvements in parcellation reveal a 29% increase in accuracy with drastic speed gains over conventional methods. The proposed method can be applied to other analysis of surface data, particularly relevant for studying neurological disorders.
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