TractOracle: towards an anatomically-informed reward function for RL-based tractography

arXiv:2403.17845v1 Announce Type: new
Abstract: 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. …

abstract accuracy agents algorithms annotated data arxiv cs.lg data function functions however knowledge machine machine learning reinforcement reinforcement learning train type