Steerable Needle

Inverse Reinforcement Learning Intra-operative Path Planning for Steerable Needle

Author(s): Elena De Momi
Inverse Reinforcement Learning Intra-operative Path Planning for Steerable Needle dziura
This work presents a safe and effective keyhole neurosurgery intra-operative planning framework for flexible neurosurgical robots. The framework is intended to support neurosurgeons during the intraoperative procedure to react to a dynamic environment. The proposed system integrates inverse reinforcement learning path planning algorithm, based on expert demonstrations, with a realistic, time-bounded simulator based on Position-based Dynamics simulation that mocks brain deformations due to catheter insertion and a simulated robotic system. Simulation results performed on a human brain dataset show that that the presented intra-operative steerable needle path planner is able to avoid anatomical obstacles while optimising surgical criteria. read more