Merging multiple input descriptors and supervisors in a deep neural
  network for tractogram filtering
Daniel Jörgens Pierre-Marc Jodoin Maxime Descoteaux Rodrigo Moreno
One of the main issues of the current tractography methods is their high
false-positive rate. Tractogram filtering is an option to remove false-positive
streamlines from tractography data in a post-processing step. In this paper, we
train a deep neural network for filtering tractography data in which every
streamline of a tractogram is classified as {\em plausible, implausible}, or
{\em inconclusive}. For this, we use four different tractogram filtering
strategies as supervisors: TractQuerier, RecobundlesX, TractSeg, and an
anatomy-inspired filter. Their outputs are combined to obtain the
classification labels for the streamlines. We assessed the importance of
different types of information along the streamlines for performing this
classification task, including the coordinates of the streamlines, diffusion
data, landmarks, T1-weighted information, and a brain parcellation. We found
that the streamline coordinates are the most relevant followed by the diffusion
data in this particular classification task.