A major limitation of minimally invasive surgery is the difficulty in accurately locating the internal anatomical structures of the target organ due to the lack of tactile feedback and transparency. Augmented reality (AR) offers a promising solution to overcome this challenge. Numerous studies have shown that combining learning-based and geometric methods can achieve accurate preoperative and intraoperative data registration. This work proposes a real-time monocular 3D tracking algorithm for post-registration tasks. The ORB-SLAM2 framework is adopted and modified for prior-based 3D tracking. The primitive 3D shape is used for fast initialization of the monocular SLAM. A pseudo-segmentation strategy is employed to separate the target organ from the background for tracking purposes, and the geometric prior of the 3D shape is incorporated as an additional constraint in the pose graph. Experiments from in-vivo and ex-vivo tests demonstrate that the proposed 3D tracking system provides robust 3D tracking and effectively handles typical challenges such as fast motion, out-of-field-of-view scenarios, partial visibility, and "organ-background" relative motion.

翻译：微创手术的一个主要局限在于，由于缺乏触觉反馈和透明度，难以精确定位目标器官的内部解剖结构。增强现实（AR）为克服这一挑战提供了有前景的解决方案。大量研究表明，将基于学习的方法与几何方法相结合，可以实现精确的术前与术中数据配准。本文提出了一种用于配准后任务的实时单目三维追踪算法。该方法采用并改进了ORB-SLAM2框架，以实现基于先验知识的三维追踪。利用原始三维形状实现单目SLAM的快速初始化。采用一种伪分割策略将目标器官与背景分离以进行追踪，并将三维形状的几何先验作为附加约束整合到位姿图中。体内与体外实验结果表明，所提出的三维追踪系统能够提供鲁棒的三维追踪，并能有效处理快速运动、视野外场景、部分可见性以及“器官-背景”相对运动等典型挑战。