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Abstract #3375

Towards a deep learning model for diffusion-aware tractogram filtering

Daniel Jörgens1, Philippe Poulin2, Rodrigo Moreno1, Pierre-Marc Jodoin2, and Maxime Descoteaux2

1KTH Royal Institute of Technology, Stockholm, Sweden, 2Université de Sherbrooke, Sherbrooke, QC, Canada

We propose a deep learning model that is able to separate a tractogram into sets of anatomically plausible and implausible streamlines. In contrast to existing methods, our model relies solely on the measured diffusion signal as an input ensuring independence of potential misalignments between subjects. The model is shown to generalize to different tractography methods and has the potential to simultaneously learn from multiple supervisor methods.

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