An attention-guided network for bilateral ventricular segmentation in pediatric echocardiography_Journal of Biomedical Engineering

Authors：

PANG Jun ¹ ,  WANG Yongxiong ¹ , CHEN Lijun ² , ZHANG Jiapeng ¹ , LIU Jinlong ^3,4 , PEI Gang ¹

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;
4. Shanghai Engineering Research Center of Virtual Reality of Structural Heart Disease, Shanghai 200120, P. R. China;

Corresponding author：

WANG Yongxiong, Email: wyxiong@usst.edu.cn

Keywords：

Pediatric echocardiography; Bilateral ventricular segmentation; Attention mechanism; Deep supervision; Multi scales

DOI：

10.7507/1001-5515.202304038

Video：

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Abstract Full text Figures/Tables Video References Cited by

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.

Citation： PANG Jun, WANG Yongxiong, CHEN Lijun, ZHANG Jiapeng, LIU Jinlong, PEI Gang. An attention-guided network for bilateral ventricular segmentation in pediatric echocardiography. Journal of Biomedical Engineering, 2023, 40(5): 928-937. doi: 10.7507/1001-5515.202304038 Copy

1.	中国心血管健康与疾病报告编写组. 中国心血管健康与疾病报告2021概要. 中国循环杂志, 2022, 37(6): 553-578.
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4.	Gahungu N, Trueick R, Bhat S, et al. Current challenges and recent updates in artificial intelligence and echocardiography. Current Cardiovascular Imaging Reports, 2020, 13: 5.
5.	Carneiro G, Nascimento J C. Combining multiple dynamic models and deep learning architectures for tracking the left ventricle endocardium in ultrasound data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(11): 2592-2607.
6.	Leclerc S, Smistad E, Pedrosa J, et al. Deep learning for segmentation using an open large-scale dataset in 2D echocardiography. IEEE Transactions on Medical Imaging, 2019, 38(9): 2198-2210.
7.	Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2015: 234-241.
8.	Moradi S, Oghli M G, Alizadehasl A, et al. MFP-Unet: a novel deep learning based approach for left ventricle segmentation in echocardiography. Physica Medica, 2019, 67: 58-69.
9.	Leclerc S, Smistad E, Østvik A, et al. LU-Net: a multistage attention network to improve the robustness of segmentation of left ventricular structures in 2-D echocardiography. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2020, 67(12): 2519-2530.
10.	Bullock J, Cuesta-Lázaro C, Quera-Bofarull A. XNet: a convolutional neural network (CNN) implementation for medical x-ray image segmentation suitable for small datasets//Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging. SPIE, 2019. DOI: 10.1117/12.2512451.
11.	Du X, Xu X, Liu H, et al. TSU-net: two-stage multi-scale cascade and multi-field fusion U-net for right ventricular segmentation. Computerized Medical Imaging and Graphics, 2021, 93: 101971.
12.	Leclerc S, Smistad E, Grenier T, et al. RU-Net: a refining segmentation network for 2D echocardiography//2019 IEEE International Ultrasonics Symposium (IUS). Glasgow: IEEE, 2019: 1160-1163.
13.	Itti L, Koch C, Niebur E. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254-1259.
14.	姚庆安,张鑫,刘力鸣,等. 融合注意力机制和多尺度特征的图像语义分割. 吉林大学学报(理学版), 2022, 60(6): 1383-1390.
15.	Hu J, Shen L, Sun G, et al. Squeeze-and-excitation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011-2023.
16.	Woo S, Park J, Lee J Y, et al. CBAM: convolutional block attention module// Proceedings of the European conference on computer vision (ECCV). 2018: 3-19. DOI: 10.1007/978-3-030-01234-2_1.
17.	Song Y, Du X, Zhang Y, et al. Two-stage segmentation network with feature aggregation and multi-level attention mechanism for multi-modality heart images. Computerized Medical Imaging and Graphics, 2022, 97: 102054.
18.	Guo L, Lei B, Chen W, et al. Dual attention enhancement feature fusion network for segmentation and quantitative analysis of paediatric echocardiography. Medical Image Analysis, 2021, 71: 102042.
19.	Zhao X, Zhang P, Song F, et al. Prior attention network for multi-lesion segmentation in medical images. IEEE Transactions on Medical Imaging, 2022, 41(12): 3812-3823.
20.	Gu R, Wang G, Song T, et al. CA-Net: Comprehensive attention convolutional neural networks for explainable medical image segmentation. IEEE Transactions on Medical Imaging, 2021, 40(2): 699-711.
21.	Oktay O, Schlemper J, Folgoc L L, et al. Attention U-net: learning where to look for the pancreas//International Conference on Medical Image Computing and Computer-assisted Intervention. Cham: Springer, 2018: 369-377.
22.	Zhou Z, Rahman Siddiquee M M, Tajbakhsh N, et al. UNet++: a nested U-Net architecture for medical image segmentation//Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (DLMIA 2018), Cham: Springer, 2018, 11045: 3-11.
23.	Chen L C, Zhu Y, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation//Proceedings of the European conference on computer vision (ECCV). 2018: 801-818.
24.	Amer A, Ye X, Janan F. ResDUnet: a deep learning-based left ventricle segmentation method for echocardiography. IEEE Access, 2021, 9: 159755-159763.

1. 中国心血管健康与疾病报告编写组. 中国心血管健康与疾病报告2021概要. 中国循环杂志, 2022, 37(6): 553-578.
2. Painchaud N, Skandarani Y, Judge T, et al. Cardiac segmentation with strong anatomical guarantees. IEEE Transactions on Medical Imaging, 2020, 39(11): 3703-3713.
3. Zhang J P, Wang Y X, Chen L J, et al. Dual-branch TransV-Net for 3D echocardiography segmentation. IEEE Transactions on Industrial Informatics, 2023, DOI: 10.1109/TII.2023.3249904.
4. Gahungu N, Trueick R, Bhat S, et al. Current challenges and recent updates in artificial intelligence and echocardiography. Current Cardiovascular Imaging Reports, 2020, 13: 5.
5. Carneiro G, Nascimento J C. Combining multiple dynamic models and deep learning architectures for tracking the left ventricle endocardium in ultrasound data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(11): 2592-2607.
6. Leclerc S, Smistad E, Pedrosa J, et al. Deep learning for segmentation using an open large-scale dataset in 2D echocardiography. IEEE Transactions on Medical Imaging, 2019, 38(9): 2198-2210.
7. Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2015: 234-241.
8. Moradi S, Oghli M G, Alizadehasl A, et al. MFP-Unet: a novel deep learning based approach for left ventricle segmentation in echocardiography. Physica Medica, 2019, 67: 58-69.
9. Leclerc S, Smistad E, Østvik A, et al. LU-Net: a multistage attention network to improve the robustness of segmentation of left ventricular structures in 2-D echocardiography. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2020, 67(12): 2519-2530.
10. Bullock J, Cuesta-Lázaro C, Quera-Bofarull A. XNet: a convolutional neural network (CNN) implementation for medical x-ray image segmentation suitable for small datasets//Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging. SPIE, 2019. DOI: 10.1117/12.2512451.
11. Du X, Xu X, Liu H, et al. TSU-net: two-stage multi-scale cascade and multi-field fusion U-net for right ventricular segmentation. Computerized Medical Imaging and Graphics, 2021, 93: 101971.
12. Leclerc S, Smistad E, Grenier T, et al. RU-Net: a refining segmentation network for 2D echocardiography//2019 IEEE International Ultrasonics Symposium (IUS). Glasgow: IEEE, 2019: 1160-1163.
13. Itti L, Koch C, Niebur E. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254-1259.
14. 姚庆安,张鑫,刘力鸣,等. 融合注意力机制和多尺度特征的图像语义分割. 吉林大学学报(理学版), 2022, 60(6): 1383-1390.
15. Hu J, Shen L, Sun G, et al. Squeeze-and-excitation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011-2023.
16. Woo S, Park J, Lee J Y, et al. CBAM: convolutional block attention module// Proceedings of the European conference on computer vision (ECCV). 2018: 3-19. DOI: 10.1007/978-3-030-01234-2_1.
17. Song Y, Du X, Zhang Y, et al. Two-stage segmentation network with feature aggregation and multi-level attention mechanism for multi-modality heart images. Computerized Medical Imaging and Graphics, 2022, 97: 102054.
18. Guo L, Lei B, Chen W, et al. Dual attention enhancement feature fusion network for segmentation and quantitative analysis of paediatric echocardiography. Medical Image Analysis, 2021, 71: 102042.
19. Zhao X, Zhang P, Song F, et al. Prior attention network for multi-lesion segmentation in medical images. IEEE Transactions on Medical Imaging, 2022, 41(12): 3812-3823.
20. Gu R, Wang G, Song T, et al. CA-Net: Comprehensive attention convolutional neural networks for explainable medical image segmentation. IEEE Transactions on Medical Imaging, 2021, 40(2): 699-711.
21. Oktay O, Schlemper J, Folgoc L L, et al. Attention U-net: learning where to look for the pancreas//International Conference on Medical Image Computing and Computer-assisted Intervention. Cham: Springer, 2018: 369-377.
22. Zhou Z, Rahman Siddiquee M M, Tajbakhsh N, et al. UNet++: a nested U-Net architecture for medical image segmentation//Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (DLMIA 2018), Cham: Springer, 2018, 11045: 3-11.
23. Chen L C, Zhu Y, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation//Proceedings of the European conference on computer vision (ECCV). 2018: 801-818.
24. Amer A, Ye X, Janan F. ResDUnet: a deep learning-based left ventricle segmentation method for echocardiography. IEEE Access, 2021, 9: 159755-159763.

Journal of Biomedical Engineering

An attention-guided network for bilateral ventricular segmentation in pediatric echocardiography

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