Anat-SFSeg: Anatomically-guided superficial fiber segmentation with point-cloud deep learning

Di Zhang, Fangrong Zong, Qichen Zhang, Yunhui Yue, Fan Zhang, Kun Zhao, Dawei Wang, Pan Wang, Xi Zhang, Yong Liu

Abstract: Highlights•We propose a novel deep learning framework Anat-SFSeg for superficial white matter fiber segmentation guided by grey matter anatomy, which performs great accuracy.•Anat-SFSeg contains a descriptor FiberAnatMap to represent both individual-level and group-level anatomical features for each streamline.•Two new metrics FARP and ARFC are proposed. They are used to quantify the proportion of fibers in anatomical brain regions and the average fiber number in each cluster, enabling the comparison of segmentation methods and assessment of inter-subject differences respectively.•Applications on Alzheimer's disease and mild cognitive impairment reveal that diffusion metrics, along with our novel metric ARFC show disorder severity associated alterations, and they are considered as potential neuroimaging biomarkers.