Anatomically constrained squeeze-and-excitation graph attention network for cortical surface parcellation. (January 2022)

• Record Type: Journal Article
• Authors: Li, Xinwei; Tan, Jia; Wang, Panyu; Liu, Hong; Li, Zhangyong; Wang, Wei
• Is Part Of: Computers in biology and medicine. Volume 140(2022)
• Publication Date: 2022-01
• Subjects: Cortical surface -- Parcellation -- Graph attention network -- Anatomical prior -- Squeeze-and-Excitation module; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.105113
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: In order to understand the organizational structures of healthy cerebral cortex and the abnormalities in neurological and psychiatric diseases, it is significant to parcellate the cortical surface. The cortical surface, however, is a highly folded complex geometric structure which challenges automatic cortical surface parcellation. Nowadays, the parcellation methods of cerebral cortex are mostly based on geometric simplification, i.e., iteratively inflating and mapping the cortical surface to a spherical surface for processing, which is time-consuming and cannot make full use of the intrinsic structural information of the original cortical surface. In this study, we proposed an anatomically constrained squeeze-and-excitation graph attention network (ASEGAT) for an end-to-end brain cortical surface parcellation on the original cortical surface manifold. The ASEGAT is formed by two graph attention modules and a squeeze-and-excitation module that incorporate self-attention and head attention for rendering features of each node. Furthermore, we designed an anatomic constraint loss to introduce the anatomical priori of regional adjacency relationships, which could improve the consistency of region labeling. We evaluated our model on a public dataset of 100 manually labeled brain surfaces. Compared with several advanced methods, the results showed that our proposed approach achieved state-of-the-art performance, obtaining an accuracy of 90.65% and a dice score of 89.00%. Highlights: Propose a novel multi-heads GAT using the SE module named ASEGAT. Present an ACLoss to improve the consistency of area labeling. The experiments on the Mindboggle dataset validate the superiority of ASEGAT.