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AutoPlan: Autonomous Surgical Planning Framework for Neurosurgery Interventions With Steerable Needles
Pranali Ingole1, Niravkumar Patel1
1Department of Engineering Design, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
Background:
Accurate path planning for minimally invasive neurosurgery is necessary to navigate complex anatomy that contains critical structures, such as blood vessels, ventricles and white-matter tracts. Planning with nonholonomic kinematics is difficult, and typical planners fail to generate kinematically feasible paths.
Methods:
AutoPlan is a deep reinforcement learning (DRL) framework that generates a needle trajectory on a magnetic resonance imaging (MRI) patient dataset. AutoPlan implements kinematic control strategies, variable curvature with fixed insertion (VCFI) and variable insertion with fixed curvature (VIFC), with random and selective start-point initialisation. Path planning was performed on 55 volumes using PPO, SAC, DDPG and RRT.
Results:
PPO (VIFC, selective start): 90.9% success, path length 45.96 14.76 mm, computation time 0.02 0.02 s. RRT: 65.5% success, path length 102.07 126.15 mm, computation time 9.72 20.29 s.
Conclusions:
AutoPlan provides consistent planning while avoiding critical structures, resulting in fast, kinematically safe trajectories.