Cheng Jiale, Zhao Fenqiang, Wu Zhengwang, Wang Ya, Yuan Xinrui, Sun Yue, Lin Weili, Wang Li, Zhang Xin, Li Gang
School of Electronic and Information Engineering, South China University of Technology, China.
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA.
Proc IEEE Int Symp Biomed Imaging. 2023 Apr;2023. doi: 10.1109/isbi53787.2023.10230447. Epub 2023 Sep 1.
In neuroimaging analysis, accurate parcellation of the extremely folded cerebellar cortex is of immense importance for both structural and functional studies. To this end, we aim to develop a novel end-to-end deep learning-based method for automatic parcellation of the cerebellar cortical surface, which has an intrinsic spherical topology. Motivated by the success of Transformer, we employ Spherical Transformer to leverage its ability to model long-range dependency. To address the nonuniform, moderate distortions during the spherical mapping of the folded cerebellar surface, we propose a Deformable Spherical Transformer, which combines the Spherical Transformer architecture with the deformable attention mechanism to adaptively concentrate on the critical and challenging regions on the spherical cerebellar surface. By comparing with other state-of-the-art algorithms, we validated the superior performance of our proposed methods.
在神经影像学分析中，对极度褶皱的小脑皮质进行准确的脑区划分对于结构和功能研究都极为重要。为此，我们旨在开发一种基于端到端深度学习的新方法，用于自动划分具有内在球形拓扑结构的小脑皮质表面。受Transformer成功的启发，我们采用球形Transformer来利用其对长程依赖进行建模的能力。为了解决折叠小脑表面球形映射过程中的不均匀、适度变形问题，我们提出了一种可变形球形Transformer，它将球形Transformer架构与可变形注意力机制相结合，以自适应地聚焦于球形小脑表面的关键和具有挑战性的区域。通过与其他现有最先进算法进行比较，我们验证了所提出方法的卓越性能。
