论文标题
矢量现场流线聚类框架用于脑纤维区域分段
Vector Field Streamline Clustering Framework for Brain Fiber Tract Segmentation
论文作者
论文摘要
脑纤维区域广泛用于研究脑疾病，这可能会更好地了解疾病如何影响大脑。脑纤维区域的分割在疾病分析中认为非常重要。在本文中，我们提出了一个新型的矢量场流简流聚类框架，以用于脑纤维区域分割。脑纤维道首先在矢量场中表达，并使用流线简化算法压缩。在简化归一化和常规的polyhedron投影之后，计算每个纤维道的高维特征并馈送到IDEC聚类算法。我们还提供IDEC聚类方法和QB聚类方法的定性和定量评估。我们的脑纤维区域聚类结果有助于研究人员对大脑结构的感知。这项工作有可能自动创建强大的纤维束模板，该模板可以有效地分割脑纤维区域，同时实现一致的解剖学识别。
Brain fiber tracts are widely used in studying brain diseases, which may lead to a better understanding of how disease affects the brain. The segmentation of brain fiber tracts assumed enormous importance in disease analysis. In this paper, we propose a novel vector field streamline clustering framework for brain fiber tract segmentations. Brain fiber tracts are firstly expressed in a vector field and compressed using the streamline simplification algorithm. After streamline normalization and regular-polyhedron projection, high-dimensional features of each fiber tract are computed and fed to the IDEC clustering algorithm. We also provide qualitative and quantitative evaluations of the IDEC clustering method and QB clustering method. Our clustering results of the brain fiber tracts help researchers gain perception of the brain structure. This work has the potential to automatically create a robust fiber bundle template that can effectively segment brain fiber tracts while enabling consistent anatomical tract identification.
