基于SLAM的3D追踪系统提升腹腔镜手术中器官定位精度
SLAM assisted 3D tracking system for laparoscopic surgery
- 结合几何先验与伪分割，实现单目实时3D追踪
- 在活体和离体实验中稳定追踪，抗快速运动与遮挡
- 适合需要精准术中导航的微创手术场景
微创手术的一大挑战是缺乏触觉反馈与视觉透明度，导致难以准确定位目标器官内部结构。增强现实（AR）为此提供潜在解决方案。研究表明，融合学习与几何方法可实现术前术后数据的精准配准。本文提出一种用于术后配准任务的实时单目3D追踪算法，基于ORB-SLAM2框架并进行改进，利用原始3D形状加速单目SLAM初始化。采用伪分割策略将目标器官与背景分离以支持追踪，并将3D形状的几何先验作为姿态图中的额外约束。在活体与离体实验中，所提3D追踪系统表现出鲁棒性，有效应对快速运动、视野外、部分可见及“器官-背景”相对运动等典型挑战。
原文摘要 · Abstract (English)
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.
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