论文标题

机器人手术远程指导通过AR进行3D场景流和手工互动

Robotic Surgery Remote Mentoring via AR with 3D Scene Streaming and Hand Interaction

论文作者

Long, Yonghao, Li, Chengkun, Dou, Qi

论文摘要

随着机器人手术的日益普及,为了患者的安全,教育变得越来越重要,迫切需要。但是,由于忙碌的临床时间表或在遥远的城市工作,经验丰富的外科医生的可及性有限,因此几乎无法为新手提供足够的教育资源。作为一种有效的方式,远程指导可以帮助解决此问题,但是传统方法仅限于纯文本,音频或2D视频,这些视频不是直观或生动的。增强现实(AR)是一种蓬勃发展的技术,被广泛用于各种教育场景,他们承诺提供视觉体验和互动教学的新可能性。在本文中,我们提出了一种新型基于AR的机器人手术远程指导系统,具有有效的3D场景可视化和自然的3D手相互作用。使用头部安装的显示器(即HoloLens),导师可以远程监视从学员的操作方面流式传输的过程。导师还可以直接提供手势,该手势向受训者转移并在手术控制台中视为指导。我们在真实手术立体声视频和常见的机器人训练任务(即PEG转移和缝合线)上全面验证了该系统。关于流式场景可视化的保真度,反馈与手工交互的准确性以及整个远程指导系统中每个组件的低延迟性,证明了令人鼓舞的结果。这项工作展示了利用AR技术为机器人手术教育提供可靠,灵活和低成本的解决方案的可行性,并具有临床应用的巨大潜力。

With the growing popularity of robotic surgery, education becomes increasingly important and urgently needed for the sake of patient safety. However, experienced surgeons have limited accessibility due to their busy clinical schedule or working in a distant city, thus can hardly provide sufficient education resources for novices. Remote mentoring, as an effective way, can help solve this problem, but traditional methods are limited to plain text, audio, or 2D video, which are not intuitive nor vivid. Augmented reality (AR), a thriving technique being widely used for various education scenarios, is promising to offer new possibilities of visual experience and interactive teaching. In this paper, we propose a novel AR-based robotic surgery remote mentoring system with efficient 3D scene visualization and natural 3D hand interaction. Using a head-mounted display (i.e., HoloLens), the mentor can remotely monitor the procedure streamed from the trainee's operation side. The mentor can also provide feedback directly with hand gestures, which is in-turn transmitted to the trainee and viewed in surgical console as guidance. We comprehensively validate the system on both real surgery stereo videos and ex-vivo scenarios of common robotic training tasks (i.e., peg-transfer and suturing). Promising results are demonstrated regarding the fidelity of streamed scene visualization, the accuracy of feedback with hand interaction, and the low-latency of each component in the entire remote mentoring system. This work showcases the feasibility of leveraging AR technology for reliable, flexible and low-cost solutions to robotic surgical education, and holds great potential for clinical applications.

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