Leveraging vision and kinematics data to improve realism of biomechanic soft tissue simulation for robotic surgery
- PDF / 611,814 Bytes
- 8 Pages / 595.276 x 790.866 pts Page_size
- 32 Downloads / 174 Views
ORIGINAL ARTICLE
Leveraging vision and kinematics data to improve realism of biomechanic soft tissue simulation for robotic surgery Jie Ying Wu1 · Peter Kazanzides1 · Mathias Unberath1 Received: 21 November 2019 / Accepted: 25 March 2020 © CARS 2020
Abstract Purpose Surgical simulations play an increasingly important role in surgeon education and developing algorithms that enable robots to perform surgical subtasks. To model anatomy, finite element method (FEM) simulations have been held as the gold standard for calculating accurate soft tissue deformation. Unfortunately, their accuracy is highly dependent on the simulation parameters, which can be difficult to obtain. Methods In this work, we investigate how live data acquired during any robotic endoscopic surgical procedure may be used to correct for inaccurate FEM simulation results. Since FEMs are calculated from initial parameters and cannot directly incorporate observations, we propose to add a correction factor that accounts for the discrepancy between simulation and observations. We train a network to predict this correction factor. Results To evaluate our method, we use an open-source da Vinci Surgical System to probe a soft tissue phantom and replay the interaction in simulation. We train the network to correct for the difference between the predicted mesh position and the measured point cloud. This results in 15–30% improvement in the mean distance, demonstrating the effectiveness of our approach across a large range of simulation parameters. Conclusion We show a first step towards a framework that synergistically combines the benefits of model-based simulation and real-time observations. It corrects discrepancies between simulation and the scene that results from inaccurate modeling parameters. This can provide a more accurate simulation environment for surgeons and better data with which to train algorithms. Keywords Soft tissue deformation · Simulation · FEM · Error correction · Robotic surgery · Deep learning
Introduction Robotic surgery has changed the way many surgeries are performed. The da Vinci Surgical System® (Intuitive Surgical Inc., Sunnyvale, CA) is the most successful example with more than 4000 systems installed around the world. Not only do robots allow physicians to perform more complex surgeries, they also open the possibility for machine learning algorithms provide aid, such as automating surgical
B
Jie Ying Wu [email protected] Peter Kazanzides [email protected] Mathias Unberath [email protected]
1
Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
subtasks. Obtaining data to train these algorithms can be difficult, including but not limited to concerns regarding patient privacy; however, before operating on patients, doctors must practice on simulators. This may provide a valuable source of data. Unfortunately, many simulators have rudimentary physics and cannot accurately model large deformations. Thus, these simulators test surgeons on simplified tasks to train agility rather than on a full surgery.
Data Loading...
