Cortical Surface Diffusion Generative Models
Zhenshan Xie Simon Dahan Logan Z. J. Williams M. Jorge Cardoso Emma C. Robinson
Cortical surface analysis has gained increased prominence, given its
potential implications for neurological and developmental disorders.
Traditional vision diffusion models, while effective in generating natural
images, present limitations in capturing intricate development patterns in
neuroimaging due to limited datasets. This is particularly true for generating
cortical surfaces where individual variability in cortical morphology is high,
leading to an urgent need for better methods to model brain development and
diverse variability inherent across different individuals. In this work, we
proposed a novel diffusion model for the generation of cortical surface
metrics, using modified surface vision transformers as the principal
architecture. We validate our method in the developing Human Connectome Project
(dHCP), the results suggest our model demonstrates superior performance in
capturing the intricate details of evolving cortical surfaces. Furthermore, our
model can generate high-quality realistic samples of cortical surfaces
conditioned on postmenstrual age(PMA) at scan.