关节镜中的无缝增强现实集成：面向关节重建与引导的流程
Seamless Augmented Reality Integration in Arthroscopy: A Pipeline for Articular Reconstruction and Guidance
AI总结：
本文提出一种仅基于单目关节镜视频的增强现实流程，结合SLAM、深度估计和3D高斯泼溅实现关节内结构重建，并提供AR测量与注释引导，在幻肢数据上取得较高重建精度和渲染质量。
摘要：
关节镜是一种用于诊断和治疗关节问题的微创外科手术。关节镜的临床工作流程通常包括通过小切口将关节镜插入关节，在此过程中，外科医生主要依靠通过关节镜进行的视觉评估来进行导航和操作。然而，关节镜有限的视野和缺乏深度感知给导航复杂关节结构以及在手术过程中实现手术精度带来了挑战。为了增强术中感知，我们提出了一种稳健的流程，该流程结合了同步定位与建图、深度估计和3D高斯泼溅，仅基于单目关节镜视频即可逼真地重建关节内结构。将3D重建扩展到增强现实（AR）应用，我们的解决方案以人在回路的方式为关节切迹测量和注释锚定提供AR辅助。与传统的运动恢复结构和神经辐射场方法相比，我们的流程在平均7分钟内以显式3D表示实现了密集3D重建和有竞争力的渲染保真度。在四个幻肢数据集上进行评估时，我们的方法平均实现了RMSE = 2.21mm的重建误差，PSNR = 32.86和SSIM = 0.89。由于我们的流程能够直接从单目关节镜实现AR重建和引导，无需任何额外数据和/或硬件，我们的解决方案可能具有增强关节镜术中感知和促进手术精度的潜力。我们的AR测量工具实现了1.59 +/- 1.81mm以内的精度，AR注释工具实现了0.721的mIoU。
英文摘要：
Arthroscopy is a minimally invasive surgical procedure used to diagnose and treat joint problems. The clinical workflow of arthroscopy typically involves inserting an arthroscope into the joint through a small incision, during which surgeons navigate and operate largely by relying on their visual assessment through the arthroscope. However, the arthroscope's restricted field of view and lack of depth perception pose challenges in navigating complex articular structures and achieving surgical precision during procedures. Aiming at enhancing intraoperative awareness, we present a robust pipeline that incorporates simultaneous localization and mapping, depth estimation, and 3D Gaussian splatting to realistically reconstruct intra-articular structures solely based on monocular arthroscope video. Extending 3D reconstruction to Augmented Reality (AR) applications, our solution offers AR assistance for articular notch measurement and annotation anchoring in a human-in-the-loop manner. Compared to traditional Structure-from-Motion and Neural Radiance Field-based methods, our pipeline achieves dense 3D reconstruction and competitive rendering fidelity with explicit 3D representation in 7 minutes on average. When evaluated on four phantom datasets, our method achieves RMSE = 2.21mm reconstruction error, PSNR = 32.86 and SSIM = 0.89 on average. Because our pipeline enables AR reconstruction and guidance directly from monocular arthroscopy without any additional data and/or hardware, our solution may hold the potential for enhancing intraoperative awareness and facilitating surgical precision in arthroscopy. Our AR measurement tool achieves accuracy within 1.59 +/- 1.81mm and the AR annotation tool achieves a mIoU of 0.721.
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。
