Abstract
Congenital heart diseases (CHD) can be detected through ultrasound imaging. Although ultrasound can be used for immediate diagnosis, doctors require considerable time to read dynamic clips; typically, physicians must continuously examine disease data from beating heart images. Most importantly, this type of diagnosis relies heavily on the expertise and experience of the diagnosing physician. This study established an ultrasound image classification with deep learning algorithms to overcome the challenges involved in CHD diagnosis. We detected the most common CHD, namely the first, second, and fourth types of ventricular septal defect (VSD). We improved the performance levels of well-known deep learning algorithms (InceptionV3, ResNet, and DenseNet). Because algorithm optimization and overfitting problems can influence the performance of deep learning algorithms, we studied some optimizer algorithms and early-stopping strategies. To enhance the solution quality, we used data augmentation methods for solving this classification problem. The selected approach was further compared with Google AutoML, which applies structure search for quality prediction. Our results revealed that the proposed deep learning algorithm was able to recognize most types of VSD. However, one type of VSD remains unconquered and warrants more advanced techniques.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Avendi, M., Kheradvar, A., Jafarkhani, H.: A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac mri. Med. Image Anal. 30, 108–119 (2016)
Bridge, C.P., Ioannou, C., Noble, J.A.: Automated annotation and quantitative description of ultrasound videos of the fetal heart. Med. Image Anal. 36, 147–161 (2017)
Carneiro, G., Nascimento, J.C.: Combining multiple dynamic models and deep learning architectures for tracking the left ventricle endocardium in ultrasound data. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2592–2607 (2013)
Carneiro, G., Nascimento, J.C., Freitas, A.: The segmentation of the left ventricle of the heart from ultrasound data using deep learning architectures and derivative-based search methods. IEEE Trans. Image Process. 21(3), 968–982 (2012)
Carvalho, J., et al.: Isuog practice guidelines (updated): sonographic screening examination of the fetal heart. Ultrasound Obstet. Gynecol. 41(3), 348–359 (2013)
Chen, H., Zheng, Y., Park, J.-H., Heng, P.-A., Zhou, S.K.: Iterative multi-domain regularized deep learning for anatomical structure detection and segmentation from ultrasound images. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 487–495. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_56
Gao, H., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
Ghesu, F.C., et al.: Marginal space deep learning: efficient architecture for volumetric image parsing. IEEE Trans. Med. Imag. 35(5), 1217–1228 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105. Curran Associates, Inc. (2012)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Moradi, M., Guo, Y., Gur, Y., Negahdar, M., Syeda-Mahmood, T.: A cross-modality neural network transform for semi-automatic medical image annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 300–307. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_35
Nascimento, J.C., Carneiro, G.: Multi-atlas segmentation using manifold learning with deep belief networks. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 867–871 IEEE (2016)
Pézard, P., et al.: Influence of ultrasonographers training on prenatal diagnosis of congenital heart diseases: a 12-year population-based study. Prenat Diagn. 28(11), 1016–1022 (2008)
Poudel, R.P.K., Lamata, P., Montana, G.: Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation. In: Zuluaga, M.A., Bhatia, K., Kainz, B., Moghari, M.H., Pace, D.F. (eds.) RAMBO/HVSMR -2016. LNCS, vol. 10129, pp. 83–94. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52280-7_8
Sundaresan, V., Bridge, C.P., Ioannou, C., Noble, J.A.: Automated characterization of the fetal heart in ultrasound images using fully convolutional neural networks. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 671–674. IEEE (2017)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Wu, M.H., Chen, H.C., Lu, C.W., Wang, J.K., Huang, S.C., Huang, S.K.: Prevalence of congenital heart disease at live birth in Taiwan. J. Pediatrics 156(5), 782–785 (2010)
Yeh, S.J., et al.: National database study of survival of pediatric congenital heart disease patients in Taiwan. J. Formos. Med. Assoc. 114(2), 159–163 (2015)
Acknowledgments
The data used in this study are restricted by the Research Ethics Review Committee of the Kaohsiung Veterans General Hospital with the number 19-CT8-10(190701-2) to protect participant privacy. We thank the Ministry of Science and Technology for supporting this research with ID MOST 107-2221-E-230-007 and MOST 108-2221-E-230-004.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, SH., Tai, IH., Chen, YH., Weng, KP., Hsieh, KS. (2021). Data Augmentation for a Deep Learning Framework for Ventricular Septal Defect Ultrasound Image Classification. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12664. Springer, Cham. https://doi.org/10.1007/978-3-030-68799-1_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-68799-1_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68798-4
Online ISBN: 978-3-030-68799-1
eBook Packages: Computer ScienceComputer Science (R0)
