  Due to imaging artifacts and low signal-to-noise ratio in ultrasound images,
automatic bone surface segmentation networks often produce fragmented
predictions that can hinder the success of ultrasound-guided computer-assisted
surgical procedures. Existing pixel-wise predictions often fail to capture the
accurate topology of bone tissues due to a lack of supervision to enforce
connectivity. In this work, we propose an orientation-guided graph
convolutional network to improve connectivity while segmenting the bone
surface. We also propose an additional supervision on the orientation of the
bone surface to further impose connectivity. We validated our approach on 1042
vivo US scans of femur, knee, spine, and distal radius. Our approach improves
over the state-of-the-art methods by 5.01% in connectivity metric.
