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.

该研究提出了一种基于强化学习的 TractOracle 系统，通过训练基于分类的奖励网络，既作为训练的奖励函数，也用于提前停止跟踪过程，从而减少虚假阳性的轨迹，从而实现了对白质纤维束轨迹的同时评估和重建，取得了近 20％的真阳性比例提高和 3 倍虚假阳性比率降低的改进及另一数据集中 2 倍至 7 倍真阳性纤维束数量的增加。