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Hierarchical Fiber Clustering Based on Multi-scale Neuroanatomical Features

机译:基于多尺度神经解剖特征的分层纤维聚类

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摘要

DTI fiber tractography inspires unprecedented understanding of brain neural connectivity by allowing in vivo probing of the brain white-matter microstructures. However, tractography algorithms often output hundreds of thousands of fibers and thus render the fiber analysis a challenging task. By partitioning a huge number of fibers into dozens of bundles, fiber clustering algorithms make the task of analyzing fiber pathways relatively much easier. However, most contemporary fiber clustering methods rely on fiber geometrical information only, ignoring the more important anatomical aspects of fibers. We propose in this paper a hierarchical atlas-based fiber clustering method which utilizes multi-scale fiber neuroanatomical features to guide the clustering. In particular, for each level of the hierarchical clustering, specific scaled ROIs at the atlas are first diffused along the fiber directions, with the spatial confidence of diffused ROIs gradually decreasing from 1 to 0. For each fiber, a fuzzy associativity vector is defined to keep track of the maximal spatial confidences that the fiber can have over all diffused ROIs, thus giving the anatomical signature of the fiber. Based on the associativity vectors and the ROI covariance matrix, the Mahalanobis distance between two fibers is then calculated for fiber clustering using spectral graph theory. The same procedure is iterated over coarse-to-fine ROI scales, leading to a hierarchical clustering of the fibers. Experimental results indicate that reasonable fiber clustering results can be achieved by the proposed method.
机译:DTI纤维束摄影术通过对体内脑白质微观结构进行探测,激发了对脑神经连通性的空前理解。但是,束层摄影算法通常会输出数十万根纤维,因此使纤维分析成为一项艰巨的任务。通过将大量光纤划分为数十束,光纤群集算法使分析光纤路径的任务相对容易得多。但是,大多数当代纤维聚类方法仅依赖于纤维几何信息,而忽略了纤维更重要的解剖学方面。我们在本文中提出了一种基于层次图谱的纤维聚类方法,该方法利用多尺度纤维神经解剖学特征来指导聚类。特别是,对于层次聚类的每个级别,首先将图集上特定比例的ROI沿光纤方向扩散,扩散的ROI的空间置信度从1逐渐减小到0。对于每个光纤,将模糊关联矢量定义为跟踪纤维在所有散布的ROI上可以具有的最大空间置信度,从而给出纤维的解剖特征。基于关联矢量和ROI协方差矩阵,然后使用光谱图理论计算两条纤维之间的马氏距离,以进行纤维聚类。在从粗到细的ROI比例上重复执行相同的过程,从而导致光纤的层次化群集。实验结果表明,所提出的方法可以实现合理的纤维聚类结果。

著录项

  • 来源
  • 会议地点 Beijing(CN);Beijing(CN)
  • 作者单位

    Department of Computer Science, University of North Carolina at Chapel Hill,Department of Radiology and BRIC, University of North Carolina at Chapel Hill;

    Department of Radiology and BRIC, University of North Carolina at Chapel Hill;

    Department of Radiology and BRIC, University of North Carolina at Chapel Hill;

    Department of Radiology and BRIC, University of North Carolina at Chapel Hill;

    Department of Radiology and BRIC, University of North Carolina at Chapel Hill;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 医用物理学;
  • 关键词

  • 入库时间 2022-08-26 14:09:57

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