Automated methods that can identify white matter bundles from large tractography datasets have several applications in neuroscience research. In these applications, clustering algorithms have shown to play an important role in the analysis and visualization of white matter structure, generating useful data which can be the basis for further studies. This work proposes FFClust, an efficient fiber clustering method for large tractography datasets containing millions of fibers. Resulting clusters describe the whole set of main white matter fascicles present on an individual brain. The method aims to identify compact and homogeneous clusters, which enables several applications. In individuals, the clusters can be used to study the local connectivity in pathological brains, while at population level, the processing and analysis of reproducible bundles, and other post-processing algorithms can be carried out to study the brain connectivity and create new white matter bundle atlases. The proposed method was evaluated in terms of quality and execution time performance versus the state-of-the-art clustering techniques used in the area. Results show that FFClust is effective in the creation of compact clusters, with a low intra-cluster distance, while keeping a good quality Davies-Bouldin index, which is a metric that quantifies the quality of clustering approaches. Furthermore, it is about 8.6 times faster than the most efficient state-of-the-art method for one million fibers dataset. In addition, we show that FFClust is able to correctly identify atlas bundles connecting different brain regions, as an example of application and the utility of compact clusters.

中文翻译：

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FFClust：用于大型纤维束成像数据集的快速光纤聚类，用于详细研究大脑连接

可以从大型纤维束成像数据集中识别白质束的自动化方法在神经科学研究中具有多种应用。在这些应用中，聚类算法在白质结构的分析和可视化中发挥着重要作用，生成的有用数据可以作为进一步研究的基础。这项工作提出了 FFClust，这是一种有效的纤维聚类方法，用于包含数百万条纤维的大型纤维束成像数据集。由此产生的簇描述了个体大脑上存在的整套主要白质束。该方法旨在识别紧凑和同质的集群，从而实现多种应用。在个体中，这些簇可用于研究病理大脑中的局部连通性，而在群体水平上，可重复束的处理和分析，可以执行其他后处理算法来研究大脑连接并创建新的白质束图谱。所提出的方法在质量和执行时间性能方面与该领域使用的最先进的聚类技术进行了评估。结果表明，FFClust 在创建紧凑集群方面是有效的，具有较低的集群内距离，同时保持了高质量的 Davies-Bouldin 指数，这是一个量化集群方法质量的指标。此外，对于 100 万纤维数据集，它比最有效的最先进方法快 8.6 倍。此外，我们展示了 FFClust 能够正确识别连接不同大脑区域的图集束，作为应用示例和紧凑集群的效用。