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Conventional computer-assisted orthopaedic navigation systems rely on thetracking of dedicated optical markers for patient poses, which makes thesurgical workflow more invasive, tedious, and expensive. Visual tracking hasrecently been proposed to measure the target anatomy in a markerless andeffortless way, but the existing methods fail under real-world occlusion causedby intraoperative interventions. Furthermore, such methods arehardware-specific and not accurate enough for surgical applications. In thispaper, we propose a RGB-D sensing-based markerless tracking method that isrobust against occlusion. We design a new segmentation network that featuresdynamic region-of-interest prediction and robust 3D point cloud segmentation.As it is expensive to collect large-scale training data with occlusioninstances, we also propose a new method to create synthetic RGB-D images fornetwork training. Experimental results show that our proposed markerlesstracking method outperforms recent state-of-the-art approaches by a largemargin, especially when an occlusion exists. Furthermore, our methodgeneralises well to new cameras and new target models, including a cadaver,without the need for network retraining. In practice, by using a high-qualitycommercial RGB-D camera, our proposed visual tracking method achieves anaccuracy of 1-2 degress and 2-4 mm on a model knee, which meets the standardfor clinical applications.

Occlusion-robust Visual Markerless Bone Tracking for Computer-Assisted Orthopaedic Surgery