  Current brain white matter fiber tracking techniques show a number of
problems, including: generating large proportions of streamlines that do not
accurately describe the underlying anatomy; extracting streamlines that are not
supported by the underlying diffusion signal; and under-representing some fiber
populations, among others. In this paper, we describe a novel autoencoder-based
learning method to filter streamlines from diffusion MRI tractography, and
hence, to obtain more reliable tractograms. Our method, dubbed FINTA (Filtering
in Tractography using Autoencoders) uses raw, unlabeled tractograms to train
the autoencoder, and to learn a robust representation of brain streamlines.
Such an embedding is then used to filter undesired streamline samples using a
nearest neighbor algorithm. Our experiments on both synthetic and in vivo human
brain diffusion MRI tractography data obtain accuracy scores exceeding the 90\%
threshold on the test set. Results reveal that FINTA has a superior filtering
performance compared to conventional, anatomy-based methods, and the
RecoBundles state-of-the-art method. Additionally, we demonstrate that FINTA
can be applied to partial tractograms without requiring changes to the
framework. We also show that the proposed method generalizes well across
different tracking methods and datasets, and shortens significantly the
computation time for large (>1 M streamlines) tractograms. Together, this work
brings forward a new deep learning framework in tractography based on
autoencoders, which offers a flexible and powerful method for white matter
filtering and bundling that could enhance tractometry and connectivity
analyses.
