Autonomous Catheterization with Open-source Simulator and Expert Trajectory
This addresses the problem of limited resources for researchers in medical robotics, though it is incremental as it builds on existing simulation and learning approaches.
The authors tackled the lack of open-source tools and data for autonomous catheterization by introducing CathSim, an open-source simulator validated against real robots, and demonstrated its effectiveness in navigation tasks, potentially accelerating research in the field.
Endovascular robots have been actively developed in both academia and industry. However, progress toward autonomous catheterization is often hampered by the widespread use of closed-source simulators and physical phantoms. Additionally, the acquisition of large-scale datasets for training machine learning algorithms with endovascular robots is usually infeasible due to expensive medical procedures. In this chapter, we introduce CathSim, the first open-source simulator for endovascular intervention to address these limitations. CathSim emphasizes real-time performance to enable rapid development and testing of learning algorithms. We validate CathSim against the real robot and show that our simulator can successfully mimic the behavior of the real robot. Based on CathSim, we develop a multimodal expert navigation network and demonstrate its effectiveness in downstream endovascular navigation tasks. The intensive experimental results suggest that CathSim has the potential to significantly accelerate research in the autonomous catheterization field. Our project is publicly available at https://github.com/airvlab/cathsim.