A Zero-Shot Reinforcement Learning Strategy for Autonomous Guidewire
  Navigation
Valentina Scarponi Michel Duprez Florent Nageotte Stéphane Cotin
Purpose: The treatment of cardiovascular diseases requires complex and
challenging navigation of a guidewire and catheter. This often leads to lengthy
interventions during which the patient and clinician are exposed to X-ray
radiation. Deep Reinforcement Learning approaches have shown promise in
learning this task and may be the key to automating catheter navigation during
robotized interventions. Yet, existing training methods show limited
capabilities at generalizing to unseen vascular anatomies, requiring to be
retrained each time the geometry changes. Methods: In this paper, we propose a
zero-shot learning strategy for three-dimensional autonomous endovascular
navigation. Using a very small training set of branching patterns, our
reinforcement learning algorithm is able to learn a control that can then be
applied to unseen vascular anatomies without retraining. Results: We
demonstrate our method on 4 different vascular systems, with an average success
rate of 95% at reaching random targets on these anatomies. Our strategy is also
computationally efficient, allowing the training of our controller to be
performed in only 2 hours. Conclusion: Our training method proved its ability
to navigate unseen geometries with different characteristics, thanks to a
nearly shape-invariant observation space.