Structure-Enhanced Attentive Learning for Spine Segmentation from Ultrasound Volume Projection Images

Rui Zhao, Zixun Huang, Tianshan Liu, Frank H.F. Leung, Sai Ho Ling, De Yang, Timothy Tin Yan Lee, Daniel P.K. Lun, Yong Ping Zheng, Kin Man Lam

Research output: Journal article publicationConference articleAcademic researchpeer-review

Abstract

Automatic spine segmentation, based on ultrasound volume projection imaging (VPI), is of great value in clinical applications to diagnose scoliosis in teenagers. In this paper, we propose a novel framework to improve the segmentation accuracy on spine images via structure-enhanced attentive learning. Since the spine bones contain strong prior knowledge of their shapes and positions in ultrasound VPI images, we propose to encode this information into the semantic representations in an attentive manner. We first revisit the self-attention mechanism in representation learning, and then present a strategy to introduce the structural knowledge into the key representation in self-attention. By this means, the network explores both the contextual and structural information in the learned features, and consequently improves the segmentation accuracy. We conduct various experiments to demonstrate that our proposed method achieves promising performance on spine image segmentation, which shows great potential in clinical diagnosis.

Original languageEnglish
Pages (from-to)1195-1199
Number of pages5
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2021-June
DOIs
Publication statusPublished - Jun 2021
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: 6 Jun 202111 Jun 2021

Keywords

  • Spine segmentation
  • Structure-enhanced attention
  • Ultrasound volume projection imaging

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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