Abstract
In this study, we introduce a novel approach for the analysis and interpretation of 3D shapes, particularly applied in the context of neuroscientific research. Our method captures 2D perspectives from various vantage points of a 3D object. These perspectives are subsequently analyzed using 2D Convolutional Neural Networks (CNNs), uniquely modified with custom pooling mechanisms.
We sought to assess the efficacy of our approach through a binary classification task involving subjects at high risk for Autism Spectrum Disorder (ASD). The task entailed differentiating between high-risk positive and high-risk negative ASD cases. To do this, we employed brain attributes like cortical thickness, surface area, and extra-axial cerebral spinal measurements. We then mapped these measurements onto the surface of a sphere and subsequently analyzed them via our bespoke method.
One distinguishing feature of our method is the pooling of data from diverse views using our icosahedron convolution operator. This operator facilitates the efficient sharing of information between neighboring views. A significant contribution of our method is the generation of gradient-based explainability maps, which can be visualized on the brain surface. The insights derived from these explainability images align with prior research findings, particularly those detailing the brain regions typically impacted by ASD. Our innovative approach thereby substantiates the known understanding of this disorder while potentially unveiling novel areas of study.
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Rodriguez, U., Prieto, JC., Styner, M. (2023). IcoConv: Explainable Brain Cortical Surface Analysis for ASD Classification. In: Wachinger, C., Paniagua, B., Elhabian, S., Li, J., Egger, J. (eds) Shape in Medical Imaging. ShapeMI 2023. Lecture Notes in Computer Science, vol 14350. Springer, Cham. https://doi.org/10.1007/978-3-031-46914-5_20
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