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One of the main issues of the current tractography methods is their highfalse-positive rate. Tractogram filtering is an option to remove false-positivestreamlines from tractography data in a post-processing step. In this paper, wetrain a deep neural network for filtering tractography data in which everystreamline of a tractogram is classified as {\em plausible, implausible}, or{\em inconclusive}. For this, we use four different tractogram filteringstrategies as supervisors: TractQuerier, RecobundlesX, TractSeg, and ananatomy-inspired filter. Their outputs are combined to obtain theclassification labels for the streamlines. We assessed the importance ofdifferent types of information along the streamlines for performing thisclassification task, including the coordinates of the streamlines, diffusiondata, landmarks, T1-weighted information, and a brain parcellation. We foundthat the streamline coordinates are the most relevant followed by the diffusiondata in this particular classification task.

Merging multiple input descriptors and supervisors in a deep neural network for tractogram filtering