Virtual Reality for Synergistic Surgical Training and Data Generation
This work addresses the need for synergistic tools in surgical simulation, enabling both training for surgeons and data generation for computer vision algorithms, though it is incremental as it combines existing features into a unified framework.
The authors tackled the lack of integrated platforms for surgical training and data generation by developing AMBF+, a cost-effective VR framework that allows users to practice surgical skills while simultaneously generating structured data like object poses and segmentation maps for algorithm development, demonstrated in a virtual drilling simulator for skull-base surgery.
Surgical simulators not only allow planning and training of complex procedures, but also offer the ability to generate structured data for algorithm development, which may be applied in image-guided computer assisted interventions. While there have been efforts on either developing training platforms for surgeons or data generation engines, these two features, to our knowledge, have not been offered together. We present our developments of a cost-effective and synergistic framework, named Asynchronous Multibody Framework Plus (AMBF+), which generates data for downstream algorithm development simultaneously with users practicing their surgical skills. AMBF+ offers stereoscopic display on a virtual reality (VR) device and haptic feedback for immersive surgical simulation. It can also generate diverse data such as object poses and segmentation maps. AMBF+ is designed with a flexible plugin setup which allows for unobtrusive extension for simulation of different surgical procedures. We show one use case of AMBF+ as a virtual drilling simulator for lateral skull-base surgery, where users can actively modify the patient anatomy using a virtual surgical drill. We further demonstrate how the data generated can be used for validating and training downstream computer vision algorithms