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.

本研究解决了在微创手术中定位目标器官内部解剖结构的困难问题。通过提出一种实时单目三维追踪算法，并在ORB-SLAM2框架基础上进行优化，研究利用伪分割策略和几何先验，在术后注册任务中实现了稳健的三维追踪。实验表明，该系统能够有效应对快速运动、视野外场景、部分可见性和“器官-背景”相对运动等挑战。

基于SLAM的腹腔镜手术三维追踪系统