Evaluation of fiber clustering methods for diffusion tensor imaging
Evaluation of fiber clustering methods for diffusion tensor imaging
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DOI：
10.1109/vis.2005.29
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发表时间：
2005-11
期刊：
影响因子：
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通讯作者：
B. Moberts;A. Vilanova;J. V. Wijk
中科院分区：
文献类型：
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作者：
B. Moberts;A. Vilanova;J. V. Wijk
纤维追踪是用于扩散张量成像（DTI）结果可视化的标准方法。如果通过完整的白色物质单独重建和可视化纤维，则显示很容易变得混乱，从而难以深入了解数据。已经提出了各种聚类技术来自动获得应该代表解剖结构的束，但尚不清楚哪种聚类方法和参数设置给出最佳结果。我们提出了一个框架，以验证聚类方法的白质纤维。聚类与用作地面实况的手动分类进行比较。对于定量评估的方法，我们开发了一种新的措施来评估地面实况和聚类之间的差异。通过向医生提供不同的聚类并询问他们的判断来验证和校准该措施。我们发现，我们的新措施的值为不同的聚类匹配以及与医生的意见。使用这个框架，我们已经评估了不同的聚类算法，包括共享最近邻聚类，这还没有被用于此目的之前。我们发现，使用层次聚类使用单链接和纤维之间的平均距离的基础上的纤维相似性度量给出了最好的结果。
Fiber tracking is a standard approach for the visualization of the results of diffusion tensor imaging (DTI). If fibers are reconstructed and visualized individually through the complete white matter, the display gets easily cluttered making it difficult to get insight in the data. Various clustering techniques have been proposed to automatically obtain bundles that should represent anatomical structures, but it is unclear which clustering methods and parameter settings give the best results. We propose a framework to validate clustering methods for white-matter fibers. Clusters are compared with a manual classification which is used as a ground truth. For the quantitative evaluation of the methods, we developed a new measure to assess the difference between the ground truth and the clusterings. The measure was validated and calibrated by presenting different clusterings to physicians and asking them for their judgement. We found that the values of our new measure for different clusterings match well with the opinions of physicians. Using this framework, we have evaluated different clustering algorithms, including shared nearest neighbor clustering, which has not been used before for this purpose. We found that the use of hierarchical clustering using single-link and a fiber similarity measure based on the mean distance between fibers gave the best results.