Leveraging Vision and Kinematics Data to Improve Realism of Biomechanic
Soft-tissue Simulation for Robotic Surgery
- URL: http://arxiv.org/abs/2003.06518v1
- Date: Sat, 14 Mar 2020 00:16:08 GMT
- Title: Leveraging Vision and Kinematics Data to Improve Realism of Biomechanic
Soft-tissue Simulation for Robotic Surgery
- Authors: Jie Ying Wu, Peter Kazanzides, Mathias Unberath
- Abstract summary: We investigate how live data acquired during any robotic endoscopic surgical procedure may be used to correct for inaccurate FEM simulation results.
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.
- Score: 13.657060682152409
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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