论文标题
超声脊柱图像中的骨骼特征分割，具有稳健性的斑点和常规闭塞噪声
Bone Feature Segmentation in Ultrasound Spine Image with Robustness to Speckle and Regular Occlusion Noise
论文作者
论文摘要
由于其低成本，无辐射和实时特征，3D超声成像对脊柱侧弯的诊断显示了很大的希望。通过超声成像获得脊柱侧弯的关键是，基于骨骼特征的对称性，准确分割骨骼区域并测量脊柱侧弯度。超声图像倾向于包含许多斑点和常规的闭塞噪声，这对于专家来说是困难，乏味且耗时的，以找出骨质功能。在本文中，我们提出了一种基于超声脊柱体积投影成像（VPI）图像的U-NET结构的稳健骨特征分割方法。提出的分割方法引入了总方差损失，以降低模型对小规模和常规闭塞噪声的敏感性。与U-NET模型相比，所提出的方法提高了骰子得分的2.3％和AUC分数的1％，并显示出对斑点和常规闭塞噪声的较高鲁棒性。
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.
