Keypoints extraction based pose estimation in optical occlusion conditions for surgical instruments

In orthopedic surgical navigation, real-time tracking of surgical instrument positions can be achieved by attaching the optical reflective markers (ORMs) in the optical tracking system (OTS) to the surgical instruments. However, this approach is susceptible to environmental obstructions, leading to a loss of crucial positional information and adversely affecting the safety of the surgical procedure. To counter this issue, the presenting study implements a YOLOv8 network to predict the 2D positional information of the ORMs from a dual camera system perspectives. Subsequently, a 3D reconstruction based on a binocular algorithm is employed to reconstruct the spatial position of the ORMs. The experimental outcomes demonstrate that the methodology proposed herein provides an improved balance between accuracy and processing speed. The performance of the method was evaluated using an artificial dataset. Two variations of the YOLOv8 were tried in this study. The YOLOv8-N model achieved an average processing speed of 33 frames per second (FPS) with a mean detection error of 8.59 pixels. In contrast, the YOLOv8-X model processed at a slower frame rate of 10 FPS in average, but with a reduced average detection error of 6.36 pixels. This indicates that the proposed method can effectively mitigate the issue of missing position information due to occlusions and offer a feasible solution for accurate tracking of moving objects in environments where occlusion is a concern.