Advancing 6-DoF Instrument Pose Estimation in Variable X-Ray Imaging Geometries

TL;DR

This paper introduces a fast, general-purpose YOLOv5-6D model for accurate 6-DoF instrument pose estimation in variable X-ray geometries, improving surgical guidance and outcomes.

Contribution

A novel YOLOv5-6D architecture for real-time, geometry-agnostic 6-DoF pose estimation in X-ray images, with demonstrated effectiveness in surgical screw localization.

Findings

Achieves 42 FPS on GPU for pose estimation.

Generalizes across different X-ray geometries and image complexities.

92.41% accuracy on bone-screw pose estimation using 0.1 ADD-S metric.

Abstract

Accurate 6-DoF pose estimation of surgical instruments during minimally invasive surgeries can substantially improve treatment strategies and eventual surgical outcome. Existing deep learning methods have achieved accurate results, but they require custom approaches for each object and laborious setup and training environments often stretching to extensive simulations, whilst lacking real-time computation. We propose a general-purpose approach of data acquisition for 6-DoF pose estimation tasks in X-ray systems, a novel and general purpose YOLOv5-6D pose architecture for accurate and fast object pose estimation and a complete method for surgical screw pose estimation under acquisition geometry consideration from a monocular cone-beam X-ray image. The proposed YOLOv5-6D pose model achieves competitive results on public benchmarks whilst being considerably faster at 42 FPS on GPU. In addition, the method generalizes across varying X-ray acquisition geometry and semantic image complexity to enable accurate pose estimation over different domains. Finally, the proposed approach is tested for bone-screw pose estimation for computer-aided guidance during spine surgeries. The model achieves a 92.41% by the 0.1 ADD-S metric, demonstrating a promising approach for enhancing surgical precision and patient outcomes. The code for YOLOv5-6D is publicly available at https://github.com/cviviers/YOLOv5-6D-Pose