Efficient Cortical Surface Parcellation via Full-Band Diffusion Learning at Individual Space

Cortical parcellation delineates the cerebral cortex into distinct regions based on anatomical and/or functional criteria, a process crucial for neuroscientific research and clinical applications. Conventional methods for cortical parcellation involve spherical mapping and complex feature computation, which are time-consuming and prone to error. Recent geometric learning approaches offer some improvements but may still depend on spherical mapping and could be sensitive to mesh variations. In this work, we present Cortex-Diffusion, a fully automatic framework for cortical parcellation on native cortical surfaces without spherical mapping or morphological feature extraction. Leveraging the DiffusionNet as its backbone, Cortex-Diffusion integrates a newly designed module for full-band spectral-accelerated spatial diffusion learning to adaptively aggregate information across highly convoluted meshes, allowing high-resolution geometric representation and accurate vertex-wise delineation. Using only raw 3D vertex coordinates, the model is compact, with merely 0.49 MB of learnable parameters. Extensive experiments on adult and infant datasets demonstrates that Cortex-Diffusion achieves superior accuracy and robustness in cortical parcellation. Our code is available at https://github.com/ladderlab-xjtu/CortexDiffusion.