FiberNeat: Unsupervised White Matter Tract Filtering.Whole-brain tractograms generated from diffusion MRI digitally represent the white matter structure of the brain and are composed of millions of streamlines.  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.  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.
FiberNeat
Individual clusters/tracts are projected into a latent space using nonlinear dimensionality reduction techniques, t-SNE and UMAP, to find spurious and outlier streamlines. 
t-SNE
Individual clusters/tracts are projected into a latent space using nonlinear dimensionality reduction techniques, t-SNE and UMAP, to find spurious and outlier streamlines. 
UMAP
In addition, outlier streamline clusters are detected using DBSCAN and then removed from the data in streamline space. 
DBSCAN