Virtual reality for synergistic surgical training and data generation. Issue 4 (4th July 2022)

• Record Type: Journal Article
• Authors: Munawar, Adnan; Li, Zhaoshuo; Kunjam, Punit; Nagururu, Nimesh; Ding, Andy S.; Kazanzides, Peter; Looi, Thomas; Creighton, Francis X.; Taylor, Russell H.; Unberath, Mathias
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 10:Issue 4(2022)
• Page Start: 366
• Page End: 374
• Publication Date: 2022-07-04
• Subjects: Surgical simulation -- virtual reality -- deep learning -- computer vision; Imaging systems in biology -- Periodicals; Imaging systems in medicine -- Periodicals; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; 616.0757
• DOI: 10.1080/21681163.2021.1999331
• Languages: English
• ISSNs: 2168-1163

Abstract

ABSTRACT: 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 that 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.