Cerebellar Surface Parcellation Based on Deformable Spherical Transformer

Accurate parcellation of the extremely folded cerebellar cortex is of immense importance for both brain structural and functional studies. Manual parcellation is time-consuming and expertise dependent, which motivates us to propose a novel end-to-end deep learning-based method for cerebellar cortical surface parcellation. Leveraging the spherical topology of the cerebellar surface, we propose the Deformable Spherical Transformer, which combines the advantages of the Spherical Transformer to extract the long-range dependency and the deformable attention mechanism to adaptively focus on the critical regions. Its superior performance has been validated by comparing with advanced algorithms with an average Dice ratio of 86.40%.