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.

中文翻译：

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TractOracle：为基于 RL 的纤维束成像提供基于解剖学的奖励函数

基于强化学习 (RL) 的纤维束成像是机器学习和经典纤维束成像算法的一种有竞争力的替代方案，因为它无需任何注释数据即可获得较高的解剖精度。然而，迄今为止用于训练 RL 智能体的奖励函数并未封装解剖学知识，这会导致智能体生成虚假的误报区域。在本文中，我们提出了一种新的 RL 纤维束记录系统 TractOracle，它依赖于经过训练以简化分类的奖励网络。该网络既用作训练期间的奖励函数，也用作提前停止跟踪过程的方法，从而减少误报流线的数量。这使得我们的系统成为一种同时评估和重建 WM 流线的独特方法。我们报告称，一个数据集上的真阳性比率提高了近 20%，假阳性比率减少了 3 倍，而另一数据集上的真阳性流线数量增加了 2 倍到 7 倍。