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
翻译:外科模拟器不仅允许对复杂程序进行规划和培训,而且还提供为算法开发生成结构化数据的能力,这些数据可以用于图像制导计算机辅助干预措施。虽然已经努力开发外科医生培训平台或数据生成引擎,但据我们所知,这两个特征尚未一起提供。我们展示了我们开发的具有成本效益和协同效应的框架,名为“Asyncronousous 多元体框架+”(AMBF+),这个框架为下游算法开发生成数据,与用户同时练习其外科技能。AMBF+在虚拟现实(VR)装置上提供立体立体显示,并为浸化外科手术模拟提供随机反馈。它还可以生成多种数据,如对象构成和分解图。AMBFE+设计了一个灵活的插件设置,允许为模拟不同的外科程序提供无干扰的扩展。我们展示了使用AMBFE+作为虚拟钻孔模拟器的例子,用户可以利用虚拟外科钻机积极修改病人的剖面。我们进一步展示了生成的数据如何用于验证和训练下游计算机视觉算法。我们进一步展示了如何使用这些数据。我们展示了如何将数据用于验证和训练。