基于Web的增强现实，具有自动缩放和实时头部跟踪功能，用于无标记神经介入术前规划和头戴式机器人针插入训练
Web-based Augmented Reality with Auto-Scaling and Real-Time Head Tracking towards Markerless Neurointerventional Preoperative Planning and Training of Head-mounted Robotic Needle Insertion
AI总结：
本文提出一种无标记Web增强现实应用，利用MediaPipe和React Three Fiber实现头部自动缩放与实时跟踪，用于神经介入术前规划和头戴式机器人针插入训练，成本低且支持协作。
摘要：
神经外科手术需要极高的精度和全面的术前规划，以确保患者获得最佳预后。尽管技术进步显著，但在该领域仍需要直观、易用的工具来增强手术准备和医学教育。传统方法往往缺乏外科医生可视化复杂手术和关键神经血管结构所需的沉浸式体验，而现有的先进解决方案可能成本高昂或需要专用硬件。本研究提出了一种新颖的无标记基于Web的增强现实（AR）应用，旨在解决神经介入术前规划和教育中的这些挑战。该应用利用MediaPipe进行精确的面部定位和分割，并使用React Three Fiber进行沉浸式3D可视化，为复杂的术前流程提供了一个直观的平台。一个虚拟的2-RPS并联定位器或Skull-Bot模型被实时投影到用户面部，以高精度模拟手术工具控制。关键功能包括能够导入头部解剖结构并自动缩放到用户尺寸，以及一旦对齐后实时自动跟踪头部运动。基于Web的特性允许多用户同时访问，促进手术期间的协作，并允许医学生观察实时手术过程。一项涉及三名参与者的初步研究通过多种头部旋转练习评估了该应用的自动缩放和自动跟踪能力。本研究通过提供一种成本效益高、易用且协作的工具来改善神经外科规划和教育，为该领域做出了贡献，可能带来更好的手术结果和更全面的医学专业人员培训。我们应用的源代码可在https://github.com/Hillllllllton/skullbot_web_ar公开获取。
英文摘要：
Neurosurgery requires exceptional precision and comprehensive preoperative planning to ensure optimal patient outcomes. Despite technological advancements, there remains a need for intuitive, accessible tools to enhance surgical preparation and medical education in this field. Traditional methods often lack the immersive experience necessary for surgeons to visualize complex procedures and critical neurovascular structures, while existing advanced solutions may be cost-prohibitive or require specialized hardware. This research presents a novel markerless web-based augmented reality (AR) application designed to address these challenges in neurointerventional preoperative planning and education. Utilizing MediaPipe for precise facial localization and segmentation, and React Three Fiber for immersive 3D visualization, the application offers an intuitive platform for complex preoperative procedures. A virtual 2-RPS parallel positioner or Skull-Bot model is projected onto the user's face in real-time, simulating surgical tool control with high precision. Key features include the ability to import and auto-scale head anatomy to the user's dimensions and real-time auto-tracking of head movements once aligned. The web-based nature enables simultaneous access by multiple users, facilitating collaboration during surgeries and allowing medical students to observe live procedures. A pilot study involving three participants evaluated the application's auto-scaling and auto-tracking capabilities through various head rotation exercises. This research contributes to the field by offering a cost-effective, accessible, and collaborative tool for improving neurosurgical planning and education, potentially leading to better surgical outcomes and more comprehensive training for medical professionals. The source code of our application is publicly available at https://github.com/Hillllllllton/skullbot_web_ar.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
