论文标题
通过深的非负基质分解聚类方法的小脑齿状核的基于拖拉术
Tractography-Based Parcellation of Cerebellar Dentate Nuclei via a Deep Nonnegative Matrix Factorization Clustering Method
论文作者
论文摘要
作为最大的人小脑核，齿状核（DN）在小脑和其他大脑的其余部分之间显着起作用。基于结构的连通性拟合有可能揭示DN的地形并实现其子区域的研究。在本文中，我们根据使用扩散MRI拖拉术的结构连接性研究了一种深度非负基质分解聚类方法（DNMFC），以对人DN进行分析。我们建议使用小脑内的一组策划的片段纤维簇来描述DN的连通性。实验是对人类连接项目50名健康成年人的扩散MRI数据进行的。与最先进的聚类方法相比，DNMFC产生的DN划分显示出跨受试者包裹的质量和一致性。
As the largest human cerebellar nucleus, the dentate nucleus (DN) functions significantly in the communication between the cerebellum and the rest of the brain. Structural connectivity-based parcellation has the potential to reveal the topography of the DN and enable the study of its subregions. In this paper, we investigate a deep nonnegative matrix factorization clustering method (DNMFC) for parcellation of the human DN based on its structural connectivity using diffusion MRI tractography. We propose to describe the connectivity of the DN using a set of curated tractography fiber clusters within the cerebellum. Experiments are conducted on the diffusion MRI data of 50 healthy adults from the Human Connectome Project. In comparison with state-of-the-art clustering methods, DN parcellations resulting from DNMFC show better quality and consistency of parcels across subjects.
