Automated Linear and Non-Linear Path Planning for Neurosurgical Interventions - Peikert, Steffen; Kunz, Christian; Fischer, Nikola; Hlavac, Michal; Pala, Andrej; Schneider, Max; Mathis-Ullrich, Franziska (Proceedings - IEEE International Conference on Robotics and Automation, 2022)
Recent advances in medical technology have produced a number of flexible instruments that are capable of traversing non-linear paths. This is of special interest in the field of neurosurgery. However, the non-rigid instruments have the disadvantage that path planning becomes increasingly difficult. In addition to anatomical risk factors, the mechanical properties and constraints of the specific instrument must also be considered. To support surgeons to deal with the increase in planning complexity, we present a novel method for both linear and arbitrary follow-the-leader flexible path planning. Our method is utilizing patient-specific image data, which is then used to generate a multi-objective problem consisting of conventional risk metrics for path planning in high-risk regions like the accumulated path cost or the distance to risk structures. Simultaneously, the path-problem is also constraint to mechanical properties of the instrument such as curvature or maximum operational length. Optimal paths can then be generated by solving a multi-objective problem by approximating the Pareto front. We show that our method can automatically generate linear and non-linear paths for neurosurgical interventions in the human brain in less than 2 minutes. Furthermore, we show that the proposed automated method generates paths with 87% reduced risk compared to standard of care plannings.
CRIS-Team Scopus Importer:2023-03-21

Lens Capsule Tearing in Cataract Surgery using Reinforcement Learning - Peter, Rebekka Charlotte; Peikert, Steffen; Haide, Ludwig; Pham, Doan Xuan Viet; Chettaoui, Tahar; Tagliabue, Eleonora; Scheikl, Paul; Fauser, Johannes; Hillenbrand, Matthias; Neumann, Gerhard; Mathis-Ullrich, Franziska (Proceedings - IEEE International Conference on Robotics and Automation, 2024)
Cataract is the leading cause of blindness worldwide with an increasing number of patients due to changing demographics, making automation an important part in future surgical treatment. In this work, we focus on a substep of cataract surgery, the Continuous Curvilinear Capsulorhexis (CCC). With a high complexity, this task is an ideal candidate for Reinforcement Learning (RL) in simulation. First, we present an interactive and physically realistic simulation based on the Finite Element Method (FEM) that mimics the tearing behavior of soft tissue during CCC. Then, we train and evaluate RL models in simulation, demonstrating that the trained policies can complete the CCC in 85% of cases. We also show that applying domain randomization techniques make the policy more robust against changes in geometrical and biomechanical boundary conditions.
CRIS-Team Scopus Importer:2024-09-06