Track-To-Learn: A general framework for tractography with deep reinforcement learning
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Supervised machine learning algorithms have been proposed to learn tractography algorithms implicitly from data, without relying on hard-to-develop anatomical priors. However, supervised learning methods rely on labelled data that is very hard to obtain. To remove the need for such data but still leverage the expressiveness of neural networks, we introduce and implement Track-To-Learn, a general framework to pose tractography as a deep reinforcement learning problem. We show that competitive results can be obtained on known data and that the learned algorithms are able to generalize far better to new, unseen data, than prior supervised learning-based tractography algorithms.