Orientation-guided Graph Convolutional Network for Bone Surface Segmentation
This work addresses the challenge of accurate bone surface segmentation for ultrasound-guided surgical procedures, representing an incremental improvement in connectivity.
The paper tackled the problem of fragmented bone surface segmentation in ultrasound images by proposing an orientation-guided graph convolutional network with additional supervision, resulting in a 5.01% improvement in connectivity metric over state-of-the-art methods.
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.