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Abstract #0336

Track-To-Learn: A general framework for tractography with deep reinforcement learning

Antoine Théberge1, Christian Desrosiers2, Maxime Descoteaux1, and Pierre-Marc Jodoin1
1Faculté des Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada, 2Département de génie logiciel et des TI, École de technologie supérieure, Montréal, QC, Canada

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.

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