  3D ultrasound imaging shows great promise for scoliosis diagnosis thanks to
its low-costing, radiation-free and real-time characteristics. The key to
accessing scoliosis by ultrasound imaging is to accurately segment the bone
area and measure the scoliosis degree based on the symmetry of the bone
features. The ultrasound images tend to contain many speckles and regular
occlusion noise which is difficult, tedious and time-consuming for experts to
find out the bony feature. In this paper, we propose a robust bone feature
segmentation method based on the U-net structure for ultrasound spine Volume
Projection Imaging (VPI) images. The proposed segmentation method introduces a
total variance loss to reduce the sensitivity of the model to small-scale and
regular occlusion noise. The proposed approach improves 2.3% of Dice score and
1% of AUC score as compared with the u-net model and shows high robustness to
speckle and regular occlusion noise.
