Bone Feature Segmentation in Ultrasound Spine Image with Robustness to Speckle and Regular Occlusion Noise
This work addresses the need for accurate scoliosis diagnosis via low-cost ultrasound imaging, though it appears incremental as it builds on existing U-net methods with specific enhancements for noise robustness.
The paper tackles the problem of segmenting bone features in 3D ultrasound spine images, which is challenging due to speckle and occlusion noise, and proposes a U-net-based method with a total variance loss that improves Dice score by 2.3% and AUC by 1% compared to a baseline U-net model.
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.