Dual Network Generative Adversarial Networks for Pediatric Echocardiography Segmentation

  • Libao Guo
  • Yujin Hu
  • Baiying Lei
  • Jie Du
  • Muyi Mao
  • Zelong Jin
  • Bei XiaEmail author
  • Tianfu WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11798)


Pediatric echocardiography is a commonly used medical imaging method for examining congenital heart disease (CHD). Accurate segmentation of pediatric echocardiography is usually used to derive quantitative measurements or biomarkers for subsequent CHD diagnosis and treatment planning. In order to achieve quality segmentation results, clinical pediatric echocardiography segmentation now is mainly performed by sonographers manually, which is time-consuming, labor-intensive, and highly dependent on the professional level of the sonographers. To address these issues, in this paper, we propose a novel convolutional neural network (CNN) architecture, called dual network generative adversarial networks (DNGAN). DNGAN consists of one generator and two discriminators, the generator uses parallel dual networks to extract more useful features to improve its performance. We use a dual discriminator to force the generator to learn more spatial features and segment the edges of the left heart more accurately. Experiments on the self-collected dataset shows that our proposed method achieves superior results over the state-of-the-art approaches and may help sonographers segment the left heart area faster and more accurately.


Congenital heart disease Pediatric echocardiography Dual network generative adversarial networks Dual discriminator Image segmentation 


  1. 1.
    Linde, D.V.D., et al.: Birth prevalence of congenital heart disease worldwide: a systematic review and meta-analysis. J. Am. Coll. Cardiol. 58, 2241–2247 (2011)CrossRefGoogle Scholar
  2. 2.
    Ma, X.j., Huang, G.Y.: Current status of screening, diagnosis, and treatment of neonatal congenital heart disease in China. World J. Pediatr. 14, 313–314 (2018)CrossRefGoogle Scholar
  3. 3.
    Jone, P.N., Gould, R., Barrett, C., Younoszai, A.K., Fonseca, B.: Data-driven quality improvement project to increase the value of the congenital echocardiographic report. Pediatr. Cardiol. 39, 726–730 (2018)CrossRefGoogle Scholar
  4. 4.
    Lopez, L., et al.: Recommendations for quantification methods during the performance of a pediatric echocardiogram: a report from the pediatric measurements writing group of the american society of echocardiography pediatric and congenital heart disease council. J. Am. Soc. Echocardiogr. 23, 465–495 (2010)CrossRefGoogle Scholar
  5. 5.
    Greenspan, H., Ginneken, B.V., Summers, R.M.: Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans. Med. Imaging 35(5), 1153–1159 (2016)CrossRefGoogle Scholar
  6. 6.
    Zyuzin, V., et al.: Identification of the left ventricle endocardial border on two-dimensional ultrasound images using the convolutional neural network Unet. In: Ural Symposium on Biomedical Engineering Radioelectronics and Information Technology (2018)Google Scholar
  7. 7.
    Leclerc, S., et al.: Deep learning for segmentation using an open large-scale dataset in 2D echocardiography. IEEE Trans. Med. Imaging (2019)Google Scholar
  8. 8.
    Goodfellow, I.J., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  9. 9.
    Xue, Y., Xu, T., Zhang, H., Long, L.R., Huang, X.L.: SegAN: adversarial network with multi-scale L1 loss for medical image segmentation. Comput. Vis. Pattern Recogn. 16, 383–392 (2018)Google Scholar
  10. 10.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Google Scholar
  11. 11.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167 (2015)
  12. 12.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science CenterShenzhen UniversityShenzhenChina
  2. 2.Ultrasound Department, Shenzhen Children HospitalHospital of Shantou UniversityShantouChina

Personalised recommendations