An efficient end-to-end CNN for segmentation of bone surfaces from ultrasound.

The application of ultrasound (US) imaging in orthopedic surgery has always been a research direction. However, the various problems of US imaging hinder the development of computer assisted orthopedic surgery guided by US. US bone segmentation has been an important yet challenging task for many clinical applications. We propose a new end-to-end fully convolution network called BoneNet for real-time and accurate segmentation of bone surface from US image. The BoneNet employs the squeeze-and-excitation residual to realize a robust feature learning. In order to speed up the segmentation, we reduce the convolution kernel and used depth-wise separable convolution to reduce network parameters. In addition, we assessed the impact of different normalization operations and loss functions on network performance. Finally, we realize the comparison of the segmentation performance and generalization ability of the existing real-time US bone surface segmentation network under the unified dataset. We achieved an average Dice coefficient of 93.03 % on segmentation performance test, and 91.25 % on the generalization ability test. The results show that our proposed method ensures the real-time performance and achieves significant improvements in accuracy, which substantially outperformed the state-of-the-art.