论文标题
骨表面分割的方向引导的图形卷积网络
Orientation-guided Graph Convolutional Network for Bone Surface Segmentation
论文作者
论文摘要
由于超声图像中的成像伪影和低信噪比，自动骨表面分割网络通常会产生零散的预测，从而阻碍超声引导的计算机辅助手术程序的成功。由于缺乏执行连通性的监督，现有的像素预测通常无法捕获骨组织的准确拓扑。在这项工作中，我们提出了一个定向引导的图形卷积网络，以提高连通性，同时分割骨表面。我们还提出了有关骨表面方向的额外监督，以进一步施加连通性。我们在1042 Vivo US扫描股骨，膝盖，脊柱和远端半径上验证了我们的方法。我们的方法将最新方法的连通性指标提高了5.01％。
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.
