Dual-task ultrasound spine transverse vertebrae segmentation network with contour regularization

Juan Lyu, Xiaojun Bi, Sunetra Banerjee, Zixun Huang, Frank H.F. Leung, Timothy Tin Yan Lee, De De Yang, Yong Ping Zheng, Sai Ho Ling

Research output: Journal article publicationJournal articleAcademic researchpeer-review

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.

Original languageEnglish
Article number101896
JournalComputerized Medical Imaging and Graphics
Volume89
DOIs
Publication statusPublished - Apr 2021

Keywords

  • 3D Ultrasound imaging
  • Medical image segmentation
  • Scoliosis
  • Shape regularization

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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