Automatic Chest CT Image Findings of Novel Coronavirus Pneumonia (COVID-19) Using U-Net Based Convolutional Neural Network

Document Type : Special Issue: The Importance of Human Computer Interaction: Challenges, Methods and Applications


1 Assistant Professor, Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, India.

2 Assistant Professor, Department of Civil Engineering, Karunya Institute of Technology and Sciences, India.


The continuing outbreak of COVID-19 pneumonia is globally concerning. Timely detection of infection ensures prompt quarantine of patient which is crucial for preventing the rapid spread of this contagious disease and also supports the patient with necessary medication. Due to the high infection rate of COVID-19, our health management system needs an automatic diagnosis tool that equips the health workers to pay immediate attention to the needy person. Chest CT is an essential imaging technique for diagnosis and staging of 2019 novel coronavirus disease (COVID-19). The identification of COVID-19 CT findings assists health workers on further clinical evaluation, especially when the findings on CT scans are trivial, the person may be recommended for Reverse-transcription polymerase chain reaction (RT-PCR) tests. Literature reported that the ground-glass opacity (GGO) with or without consolidation are dominant CT findings in COVID-19 patients. In this paper, the U-Net based segmentation approach is proposed to automatically segment and analyze the GGO and consolidation findings in the chest CT scan. The performance of this system is evaluated by comparing the auto-segmented infection regions with the manually-outlines ones on 100 axial chests CT scans of around 40 COVID-19 patients from SIRM dataset. The proposed U-Net with pre-process approach yields specificity of 0.91 ± 0.09 and sensitivity of 0.87 ± 0.07 on segmenting GGO region and specificity of 0.81 ± 0.13 and sensitivity of 0.44 ± 0.17 on segmenting consolidation region. Also the experimental results confirmed that the automatic detection method identifies the CT finding with a precise opacification percentage from the chest CT image.


Agnes, S. A., Anitha, J., & Peter, J. D. (2018). Automatic lung segmentation in low-dose chest CT scans using convolutional deep and wide network (CDWN). Neural Computing and Applications, 1–11.
Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, Wenzhi., Qian.,  Xia & Liming. (2020). Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology, 200642.
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495.
Bernheim, A., Mei, X., Huang, M., Yang, Y., Fayad, Z. A., Zhang, N. (2020). Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection. Radiology, 200463.
Commission, C. N. H., (2020). Diagnosis and treatment of pneumonitis caused by new coronavirus (trial version 6). Beijing: China National Health Commission.
Fang, Y., Zhang, H., Xie, J., Lin, M., Ying, L., Pang, P., & Ji, W. (2020). Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology, 200432.
Hao, W., & Li, M. (2020). Clinical diagnostic value of CT imaging in COVID-19 with multiple negative RT-PCR testing. Travel Medicine and Infectious Disease.
Hui, D. S., Azhar, E. I., Madani, T. A., Ntoumi, F., Kock, R., Dar, O. (2020). The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health—The latest 2019 novel coronavirus outbreak in Wuhan, China. International Journal of Infectious Diseases, 91, 264.
Kanne, J. P. (2020). Chest CT findings in 2019 novel coronavirus (2019-nCoV) infections from Wuhan, China: key points for the radiologist. Radiological Society of North America.
Khagi, B., & Kwon, G.-R. (2018). Pixel-Label-Based Segmentation of Cross-Sectional Brain MRI Using Simplified SegNet Architecture-Based CNN. Journal of Healthcare Engineering, 2018.
Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.-W., & Heng, P.-A. (2018). H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Transactions on Medical Imaging, 37(12), 2663–2674.
Long, C., Xu, H., Shen, Q., Zhang, X., Fan, B., Wang, C., Li, H. (2020). Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT? European Journal of Radiology, 108961.
Mahase, E. (2020). China coronavirus: WHO declares international emergency as death toll exceeds 200. Bmj, 368, m408.
Meng, H., Xiong, R., He, R., Lin, W., Hao, B., Zhang, L. (2020). CT imaging and clinical course of asymptomatic cases with COVID-19 pneumonia at admission in Wuhan, China. Journal of Infection.
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, 234–241.
Shelhamer, E., Long, J., & Darrell, T. (2017). Fully convolutional networks for semantic segmentation. IEEE Annals of the History of Computing, (04), 640–651.
Ye, Zheng et al. 2020. “Chest CT Manifestations of New Coronavirus Disease 2019 (COVID-19): A Pictorial Review.” European radiology: 1–9.
Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (pp. 3–11). Springer.
Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J. (2020). A novel coronavirus from patients with pneumonia in China, 2019. New England Journal of Medicine.