| Paper ID | BIO-8.4 |
| Paper Title |
STRUCTURE-ENHANCED ATTENTIVE LEARNING FOR SPINE SEGMENTATION FROM ULTRASOUND VOLUME PROJECTION IMAGES |
| Authors |
Rui Zhao, Zixun Huang, Tianshan Liu, Frank H.F. Leung, The Hong Kong Polytechnic University, Hong Kong SAR China; Sai Ho Ling, University of Technology Sydney, Australia; De Yang, Timothy Tin-Yan Lee, Daniel P.K. Lun, Yong-Ping Zheng, Kin-Man Lam, The Hong Kong Polytechnic University, Hong Kong SAR China |
| Session | BIO-8: Biological Image Analysis |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Biomedical Imaging and Signal Processing: [BIO-BIA] Biological image analysis |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
