Libao Guo
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Libao Guo has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Alejandro Federico Frangi
University of Manchester
1 shared publication - Baiying Lei
Shenzhen University
1 shared publication - Bei Xia
Shenzhen Children's Hospital, China
1 shared publication - Jie Du
Shenzhen University
1 shared publication - Muyi Mao
Department of Medical Ultrasonics, The Seventh Affiliated Hospital of Sun Yat-sen University , Zhenyuan Road, Guangming District, Shenzhen 518000 , Guangdong
1 shared publication - Tianfu Wang
Shenzhen University
1 shared publication
Latest Publications
- Open Access
- 3 February 2020
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 8, 29176-29187
AIDAN: An Attention-Guided Dual-Path Network for Pediatric Echocardiography Segmentation
Accurate segmentation of pediatric echocardiography images is essential for a wide range of diagnostic and pre-interventional planning, but remains challenging (e.g., low signal to noise ratio and internal variability in heart appearance). To address these problems, in this paper, we propose a novel Cardiac Attention-guided Dual-path Network (i.e., AIDAN). AIDAN comprises a convolutional block attention module (CBAM) attached to a spatial (i.e., SPA) and context paths (i.e., CPA), which can guide the network and learn the most discriminative features. The spatial path captures low-level spatial features, and the context path is designed to exploit high-level context. Finally, features learned from the two paths are fused efficiently using a specially designed feature fusion module (FFM), and these are used to predict the final segmentation map. We experiment on a self-collected dataset of 127 pediatric echocardiography cases which are videos containing at least a complete cardiac cycle, and obtain a Dice coefficient of 0.951 and 0.914, in the left ventricle and atrium segments, respectively. AIDAN outperforms other state-of-the-art methods and has great potential for pediatric echocardiography images analysis.
- 3 February 2020
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 8, 29176-29187
Open AccessAIDAN: An Attention-Guided Dual-Path Network for Pediatric Echocardiography Segmentation
Accurate segmentation of pediatric echocardiography images is essential for a wide range of diagnostic and pre-interventional planning, but remains challenging (e.g., low signal to noise ratio and internal variability in heart appearance). To address these problems, in this paper, we propose a novel Cardiac Attention-guided Dual-path Network (i.e., AIDAN). AIDAN comprises a convolutional block attention module (CBAM) attached to a spatial (i.e., SPA) and context paths (i.e., CPA), which can guide the network and learn the most discriminative features. The spatial path captures low-level spatial features, and the context path is designed to exploit high-level context. Finally, features learned from the two paths are fused efficiently using a specially designed feature fusion module (FFM), and these are used to predict the final segmentation map. We experiment on a self-collected dataset of 127 pediatric echocardiography cases which are videos containing at least a complete cardiac cycle, and obtain a Dice coefficient of 0.951 and 0.914, in the left ventricle and atrium segments, respectively. AIDAN outperforms other state-of-the-art methods and has great potential for pediatric echocardiography images analysis.