Dual-task ultrasound spine transverse vertebrae segmentation network with contour regularization. (April 2021) - 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
Highlights: Established a dataset for spine segmentation of ultrasound scoliosis images. Build a dual-task spine transverse vertebrae segmentation network based on U-Net. An auxiliary shape regularization task is used to assist the semantic segmentation. Fused the features of two-stream to enhance their representation abilities. Imported ASPP structure which also fused features of auxiliar network. Obtained a promising result and are the first team to segment them automatically. Abstract: 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.