SurgTrack: CAD-Free 3D Tracking of Real-world Surgical Instruments

TL;DR

SurgTrack is a novel two-stage, CAD-free 3D surgical instrument tracking method that enhances robustness and accuracy in real-world scenarios by combining SDF-based registration and posture graph optimization.

Contribution

The paper introduces SurgTrack, a new CAD-free 3D tracking approach using SDF modeling and posture graph optimization, addressing occlusion and texture challenges in surgical navigation.

Findings

Outperforms state-of-the-art methods in accuracy and robustness.

Demonstrates effective tracking under occlusion and weak texture conditions.

Provides a comprehensive dataset for surgical instrument tracking evaluation.

Abstract

Vision-based surgical navigation has received increasing attention due to its non-invasive, cost-effective, and flexible advantages. In particular, a critical element of the vision-based navigation system is tracking surgical instruments. Compared with 2D instrument tracking methods, 3D instrument tracking has broader value in clinical practice, but is also more challenging due to weak texture, occlusion, and lack of Computer-Aided Design (CAD) models for 3D registration. To solve these challenges, we propose the SurgTrack, a two-stage 3D instrument tracking method for CAD-free and robust real-world applications. In the first registration stage, we incorporate an Instrument Signed Distance Field (SDF) modeling the 3D representation of instruments, achieving CAD-freed 3D registration. Due to this, we can obtain the location and orientation of instruments in the 3D space by matching the video stream with the registered SDF model. In the second tracking stage, we devise a posture graph optimization module, leveraging the historical tracking results of the posture memory pool to optimize the tracking results and improve the occlusion robustness. Furthermore, we collect the Instrument3D dataset to comprehensively evaluate the 3D tracking of surgical instruments. The extensive experiments validate the superiority and scalability of our SurgTrack, by outperforming the state-of-the-arts with a remarkable improvement. The code and dataset are available at https://github.com/wenwucode/SurgTrack.