Seamless Augmented Reality Integration in Arthroscopy: A Pipeline for Articular Reconstruction and Guidance

TL;DR

This paper introduces a novel pipeline that uses monocular arthroscope video to perform dense 3D reconstruction and AR guidance, enhancing intraoperative awareness and surgical precision in arthroscopy.

Contribution

The authors present a new pipeline combining SLAM, depth estimation, and 3D Gaussian splatting for real-time 3D reconstruction and AR guidance from monocular arthroscopy, outperforming traditional methods.

Findings

Achieves dense 3D reconstruction in 7 minutes on average.

Reconstruction error RMSE of 2.21mm on phantom datasets.

AR measurement accuracy within 1.59mm with mIoU of 0.721.

Abstract

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.