Chandio Bramsh Qamar, Chattopadhyay Tamoghna, Owens-Walton Conor, Reina Julio E Villalon, Nabulsi Leila, Thomopoulos Sophia I, Garyfallidis Eleftherios, Thompson Paul M
Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:5055-5061. doi: 10.1109/EMBC48229.2022.9870877.
Whole-brain tractograms generated from diffusion MRI digitally represent the white matter structure of the brain and are composed of millions of streamlines. Such tractograms can have false positive and anatomically implausible streamlines. To obtain anatomically relevant streamlines and tracts, supervised and unsupervised methods can be used for tractogram clustering and tract extraction. Here we propose FiberNeat, an unsupervised white matter tract filtering method. FiberNeat takes an input set of streamlines that could either be unlabeled clusters or labeled tracts. Individual clusters/tracts are projected into a latent space using nonlinear dimensionality reduction techniques, t-SNE and UMAP, to find spurious and outlier streamlines. In addition, outlier streamline clusters are detected using DBSCAN and then removed from the data in streamline space. We performed quantitative comparisons with expertly delineated tracts. We ran FiberNeat on 131 participants' data from the ADNI3 dataset. We show that applying FiberNeat as a filtering step after bundle segmentation improves the quality of extracted tracts and helps improve tractometry.
从扩散磁共振成像生成的全脑纤维束图以数字方式表示大脑的白质结构，由数百万条纤维束组成。这样的纤维束图可能存在假阳性和解剖学上不合理的纤维束。为了获得与解剖学相关的纤维束和纤维道，可以使用监督和无监督方法进行纤维束图聚类和纤维道提取。在此，我们提出了FiberNeat，一种无监督的白质纤维束过滤方法。FiberNeat接受一组输入的纤维束，这些纤维束可以是未标记的聚类或已标记的纤维道。使用非线性降维技术t-SNE和UMAP将各个聚类/纤维道投影到潜在空间中，以找到虚假和异常的纤维束。此外，使用DBSCAN检测异常纤维束聚类，然后从纤维束空间的数据中去除。我们与专家划定的纤维道进行了定量比较。我们在来自ADNI3数据集的131名参与者的数据上运行了FiberNeat。我们表明，在束分割后将FiberNeat用作过滤步骤可提高提取纤维道的质量，并有助于改善纤维道测量。
