TractOracle: towards an anatomically-informed reward function for
  RL-based tractography
Antoine Théberge Maxime Descoteaux Pierre-Marc Jodoin
Reinforcement learning (RL)-based tractography is a competitive alternative
to machine learning and classical tractography algorithms due to its high
anatomical accuracy obtained without the need for any annotated data. However,
the reward functions so far used to train RL agents do not encapsulate
anatomical knowledge which causes agents to generate spurious false positives
tracts. In this paper, we propose a new RL tractography system, TractOracle,
which relies on a reward network trained for streamline classification. This
network is used both as a reward function during training as well as a mean for
stopping the tracking process early and thus reduce the number of false
positive streamlines. This makes our system a unique method that evaluates and
reconstructs WM streamlines at the same time. We report an improvement of true
positive ratios by almost 20\% and a reduction of 3x of false positive ratios
on one dataset and an increase between 2x and 7x in the number true positive
streamlines on another dataset.