1(Institute of Information Processing and Automation,School of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China) 2(Zhejiang Provincial United Key Laboratory of Embedded Systems,Hangzhou 310023,China) 3(Department of Neurosurgery,The First Affiliated Hospital of Wenzhou Medical University,Wenzhou 325000,Zhejiang,China) 4(Zhejiang Provincial Key Laboratory of Aging and Neurological Disorder Research,Wenzhou 325000,Zhejiang,China)
Abstract：A common problem in the process of trigeminal nerve fiber tractography is artificial dependence,mainly including artificial rendering of region of interest (ROI) and manual screening of target fibers,which generally results in uncertainty and data errors. To ovecome this problem,a data-driven automatic trigeminal nerve fiber segmentation algorithm was proposed in this paper. A data-driven fiber clustering atlas was established based on the fiber data of several groups of brain samples,which automatically segmented the fiber data of new samples and directly obtained the trigeminal nerve fibers. In experiments,25 groups of healthy young individuals were selected as samples. Firstly,the brainstem was extracted by FSL software segmentation tool as ROI for deterministic fiber tracking. Secondly,a data-driven clustering atlas of fibers was created by multi-sample registration and spectral clustering of 20 groups of fibers. According to the tiny characteristics of trigeminal nerve,the trigeminal nerve fibers were labeled by secondary classification of brainstem fibers in the process of establishing fiber atlas. Finally,new sample data of 5 groups of healthy young people were selected,and their brainstem fiber data were automatically segmented using fiber atlas to obtain trigeminal nerve fiber bundles,and theweighted Dice coefficient between the results of automatic segmentation and manual segmentation of the same sample data was calculated. Results showed that the proposed method successfully segmented 5 sets of trigeminal nerve fiber bundles while the conventional manual method successfully identified 4 sets. The weighted Dice coefficients between the two results were 0.865,0.939,0.824,and 0.942. These results showed that this method can effectively avoid the influence of human factors,and greatly improve the work efficiency of neurosurgeons and cranial nerve researchers.
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