An attention-guided network for bilateral ventricular segmentation in pediatric echocardiography.
10.7507/1001-5515.202304038
- Author:
Jun PANG
1
;
Yongxiong WANG
1
;
Lijun CHEN
2
;
Jiapeng ZHANG
1
;
Jinlong LIU
3
;
Gang PEI
1
Author Information
1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China.
2. Department of Pediatric Cardiology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai 200120, P. R. China.
3. Department of Cardiothoracic Surgery and the Institute of Pediatric Translational Medicine, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai 200120, P. R. China.
- Publication Type:Journal Article
- Keywords:
Attention mechanism;
Bilateral ventricular segmentation;
Deep supervision;
Multi scales;
Pediatric echocardiography
- MeSH:
Adult;
Humans;
Child;
Heart Ventricles/diagnostic imaging*;
Echocardiography;
Image Processing, Computer-Assisted
- From:
Journal of Biomedical Engineering
2023;40(5):928-937
- CountryChina
- Language:Chinese
-
Abstract:
Accurate segmentation of pediatric echocardiograms is a challenging task, because significant heart-size changes with age and faster heart rate lead to more blurred boundaries on cardiac ultrasound images compared with adults. To address these problems, a dual decoder network model combining channel attention and scale attention is proposed in this paper. Firstly, an attention-guided decoder with deep supervision strategy is used to obtain attention maps for the ventricular regions. Then, the generated ventricular attention is fed back to multiple layers of the network through skip connections to adjust the feature weights generated by the encoder and highlight the left and right ventricular areas. Finally, a scale attention module and a channel attention module are utilized to enhance the edge features of the left and right ventricles. The experimental results demonstrate that the proposed method in this paper achieves an average Dice coefficient of 90.63% in acquired bilateral ventricular segmentation dataset, which is better than some conventional and state-of-the-art methods in the field of medical image segmentation. More importantly, the method has a more accurate effect in segmenting the edge of the ventricle. The results of this paper can provide a new solution for pediatric echocardiographic bilateral ventricular segmentation and subsequent auxiliary diagnosis of congenital heart disease.
