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Abstract #3218

A Generative Model of Cortical Surfaces

Chengche Tsai1, Junjie Zhao2, Guoye Lin3, Sahar Ahmad4, and Pew-Thian Yap3
1Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, 2Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, 3Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, 4University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Cortical Surface

Motivation: High-quality cortical surface enables the study of cortical thickness, area, and folding patterns, which are associated with neurodegeneration, developmental disorders, and age-related diseases.

Goal(s): To develop a generative model for geometrically plausible cortical surfaces.

Approach: Using 541 sets of static velocity fields that warp a spherical mesh template to cortical surfaces, we developed a VQ-VAE model to learn a latent codebook from these SVFs. We then trained a masked RNN to model these latent codebook, which generates new codes for the VQ-VAE that decodes them into new SVFs.

Results: We qualitatively compared the cortical surfaces by different model configurations to evaluate their effectiveness.

Impact: This cortical surface generative model can produce a large number of cortical surfaces for training deep learning models and conducting neuroimaging studies.

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Keywords