Abstract
In 2021, the COVID-19 is still widespread around the world, which has a great impact on peopleʼs daily lives. However, there is still a lack of research on the fast segmentation of lung infections caused by COVID-19. The segmentation of the COVID-19- infected region from the lung CT is of great significance for the diagnosis and care of patients. In this paper, attention gate residual U-Net (AGRU-Net) based on residual network and attention gates is proposed for the segmentation. As COVID-19- infected regions varies greatly from one to another, the deeper network is needed to extract segmentation features. The residual unit is an effective solution to the degradation problem of deeper network. The addition of attention gates to U-Net suppresses irrelevant areas in the image for more significant segmentation characteristics. In this paper, the experiments on a public COVID-19CT dataset show that AGRU-Net has good performance in the segmentation of COVID-19- infected region.
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
Similar content being viewed by others
References
Shan, F., Gao, Y., Wang, J., et al.: Lung infection quantification of COVID-19 in CT images with deep learning. arXiv:2003.04655 (2020)
Shi, F., Xia, L., Shan, F., et al.: Large-scale screening of COVID-19 from community acquired pneumonia using infection size-aware classification. arXiv:2003.09860 (2020)
Cheng, Z., Lu, Y., Cao, Q., et al.: Clinical features and chest CT manifestations of coronavirus disease 2019 (COVID-19) in a single-center study in Shanghai, China. Am. J. Roentgenol. 215(1), 1–6 (2020)
Fang, Y., Zhang, H., Xie, J., et al.: Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology 296(2), 200432 (2020)
Shi, F., Wang, J., Shi, J., et al.: Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19. IEEE Rev. Biomed. Eng. 99, 1 (2020)
Fan, D.-P., Zhou, T., et al.: Inf-Net: automatic COVID-19 lung infection segmentation from CT images. IEEE Trans. Med. Imag. 39(8), 2626–2637 (2020)
Ye, C., Wang, W., Zhang, S., et al.: Multi-depth fusion network for whole-heart CT image segmentation. IEEE Access 7, 23421–23429 (2019)
Liu, Z., Song, Y.-Q., et al.: Liver CT sequence segmentation based with improved U-Net and graph cut. Expert Syst. Appl. 126(JUL), 54–63 (2019)
Dong, X., Lei, Y., Wang, T., et al.: Automatic multi-organ segmentation in thorax CT images using U-Net-GAN. Med. Phys. 46(5), 2157–2168 (2019)
He, K., Sun, J.: Convolutional neural networks at constrained time cost. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5353–5360 (2015)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2015)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning, pp. 448–456 (2015)
Wang, F., Jiang, M., Qian, C., et al.: Residual attention network for image classification. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6450–6458 (2017)
Abraham, N., Khan, N.M.: A novel focal tversky loss function with improved attention U-Net for lesion segmentation. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 683–687 (2018)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. Comp. Sci. arXiv:1409.0473 (2014)
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). https://doi.org/10.1007/978-3-319-24574-4_28
Wang, B., Jin, S., Yan, Q., et al.: AI-assisted CT imaging analysis for COVID-19 screening: building and deploying a medical AI system. Appl. Soft Comp. (2020)
Zhou, T., Canu, S., Ruan, S.: An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism. arXiv:2004.06673 (2020)
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)
Oktay, O., Schlemper, J., Folgoc, L.L., et al.: Attention U-Net: learning where to look for the pancreas. arXiv:1804.03999 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zuo, Q., Chen, S., Wang, Z. (2021). Attention Residual Convolution Neural Network Based on U-Net for COVID-19 Lung Infection Segmentation. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1451. Springer, Singapore. https://doi.org/10.1007/978-981-16-5940-9_26
Download citation
DOI: https://doi.org/10.1007/978-981-16-5940-9_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-5939-3
Online ISBN: 978-981-16-5940-9
eBook Packages: Computer ScienceComputer Science (R0)