Diffusion MRI is the preferred non-invasive in vivo modality for the study of brain white matter connections. Tractography datasets contain 3D streamlines that can be analyzed to study the main brain white matter tracts. Fiber clustering methods have been used to automatically group similar fibers into clusters. However, due to inter-subject variability and artifacts, the resulting clusters are difficult to process for finding common connections across subjects, specially for superficial white matter. We present an automatic method for labeling of short association bundles on a group of subjects. The method is based on an intra-subject fiber clustering that generates compact fiber clusters. Posteriorly, the clusters are labeled based on the cortical connectivity of the fibers, taking as reference the Desikan–Killiany atlas, and named according to their relative position along one axis. Finally, two different strategies were applied and compared for the labeling of inter-subject bundles: a matching with the Hungarian algorithm, and a well-known fiber clustering algorithm, called QuickBundles. Individual labeling was executed over four subjects, with an execution time of 3.6 min. An inspection of individual labeling based on a distance measure showed good correspondence among the four tested subjects. Two inter-subject labeling were successfully implemented and applied to 20 subjects and compared using a set of distance thresholds, ranging from a conservative value of 10 mm to a moderate value of 21 mm. Hungarian algorithm led to a high correspondence, but low reproducibility for all the thresholds, with 96 s of execution time. QuickBundles led to better correspondence, reproducibility and short execution time of 9 s. Hence, the whole processing for the inter-subject labeling over 20 subjects takes 1.17 h. We implemented a method for the automatic labeling of short bundles in individuals, based on an intra-subject clustering and the connectivity of the clusters with the cortex. The labels provide useful information for the visualization and analysis of individual connections, which is very difficult without any additional information. Furthermore, we provide two fast inter-subject bundle labeling methods. The obtained clusters could be used for performing manual or automatic connectivity analysis in individuals or across subjects.

中文翻译：

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基于聚类和皮质表面信息的自动分组全脑短关联纤维束标记。

扩散 MRI 是研究脑白质连接的首选非侵入性体内模式。纤维束成像数据集包含 3D 流线，可对其进行分析以研究主要大脑白质纤维束。纤维聚类方法已用于自动将相似的纤维分组为簇。然而，由于受试者间的变异性和伪影，所得到的簇很难处理以寻找受试者之间的共同联系，特别是对于浅层白质。我们提出了一种自动方法来标记一组主题的短关联束。该方法基于生成紧凑纤维簇的对象内纤维聚类。随后，这些簇根据纤维的皮质连接性进行标记，并参考 Desikan-Killiany 图集，并根据它们沿一个轴的相对位置进行命名。最后，应用并比较了两种不同的策略来标记主体间捆绑：匈牙利算法的匹配和著名的纤维聚类算法，称为 QuickBundles。对四名受试者进行单独标记，执行时间为 3.6 分钟。基于距离测量的个体标签检查显示，四名测试对象之间具有良好的对应性。两次受试者间标记已成功实施并应用于 20 名受试者，并使用一组距离阈值（范围从保守值 10 毫米到中等值 21 毫米）进行比较。匈牙利算法导致所有阈值的一致性较高，但再现性较低，执行时间为 96 秒。QuickBundles 带来了更好的对应性、可重复性和 9 秒的短执行时间。因此，超过20个受试者的受试者间标记的整个处理需要1.17小时。我们实现了一种基于受试者内聚类以及聚类与皮层的连接来自动标记个体短束的方法。标签为各个连接的可视化和分析提供了有用的信息，如果没有任何附加信息，这是非常困难的。此外，我们提供了两种快速的主题间捆绑标记方法。获得的聚类可用于对个体或跨受试者进行手动或自动连接分析。