Lyu Juan, Bi Xiaojun, Banerjee Sunetra, Huang Zixun, Leung Frank H F, Lee Timothy Tin-Yan, Yang De-De, Zheng Yong-Ping, Ling Sai Ho
College of Information and Communication Engineering, Harbin Engineering University, Harbin, China.
College of Information and Communication Engineering, Harbin Engineering University, Harbin, China; College of Information Engineering, Minzu University of China, Beijing, China.
Comput Med Imaging Graph. 2021 Apr;89:101896. doi: 10.1016/j.compmedimag.2021.101896. Epub 2021 Mar 15.
3D ultrasound imaging has become one of the common diagnosis ways to assess scoliosis since it is radiation-free, real-time, and low-cost. Spine curvature angle measurement is an important step to assess scoliosis precisely. One way to calculate the angle is using the vertebrae features of the 2-D coronal images to identify the most tilted vertebrae. To do the measurement, the segmentation of the transverse vertebrae is an important step. In this paper, we propose a dual-task ultrasound transverse vertebrae segmentation network (D-TVNet) based on U-Net. First, we arrange an auxiliary shape regularization network to learn the contour segmentation of the bones. It improves the boundary segmentation and anti-interference ability of the U-Net by fusing some of the features of the auxiliary task and the main task. Then, we introduce the atrous spatial pyramid pooling (ASPP) module to the end of the down-sampling stage of the main task stream to improve the relative feature extraction ability. To further improve the boundary segmentation, we extendedly fuse the down-sampling output features of the auxiliary network in the ASPP. The experiment results show that the proposed D-TVNet achieves the best dice score of 86.68% and the mean dice score of 86.17% based on cross-validation, which is an improvement of 5.17% over the baseline U-Net. An automatic ultrasound spine bone segmentation network with promising results has been achieved.
三维超声成像因其无辐射、实时且低成本，已成为评估脊柱侧弯的常见诊断方法之一。脊柱曲率角度测量是精确评估脊柱侧弯的重要步骤。计算该角度的一种方法是利用二维冠状图像的椎骨特征来识别最倾斜的椎骨。为进行测量，横断椎骨的分割是重要的一步。在本文中，我们提出了一种基于U-Net的双任务超声横断椎骨分割网络（D-TVNet）。首先，我们安排一个辅助形状正则化网络来学习骨骼的轮廓分割。它通过融合辅助任务和主要任务的一些特征，提高了U-Net的边界分割和抗干扰能力。然后，我们将空洞空间金字塔池化（ASPP）模块引入到主要任务流的下采样阶段末尾，以提高相对特征提取能力。为进一步改善边界分割，我们在ASPP中扩展融合了辅助网络的下采样输出特征。实验结果表明，所提出的D-TVNet在交叉验证的基础上实现了最佳骰子分数86.68%和平均骰子分数86.17%，比基线U-Net提高了5.17%。已实现了一个具有良好结果的自动超声脊柱骨分割网络。
