COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet

Abstract

Abstract Background Currently, there is an urgent need for efficient tools to assess the diagnosis of COVID-19 patients. In this paper, we present feasible solutions for detecting and labeling infected tissues on CT lung images of such patients. Two structurally-different deep learning techniques, and , are investigated for semantically segmenting infected tissue regions in CT lung images. Methods We propose to use two known deep learning networks, and , for image tissue classification. is characterized as a scene segmentation network and as a medical segmentation tool. Both networks were exploited as binary segmentors to discriminate between infected and healthy lung tissue, also as multi-class segmentors to learn the infection type on the lung. Each network is trained using seventy-two data images, validated on ten images, and tested against the left eighteen images. Several statistical scores are calculated for the results and tabulated accordingly. Results The results show the superior ability of in classifying infected/non-infected tissues compared to the other methods (with 0.95 mean accuracy), while the shows better results as a multi-class segmentor (with 0.91 mean accuracy). Conclusion Semantically segmenting CT scan images of COVID-19 patients is a crucial goal because it would not only assist in disease diagnosis, also help in quantifying the severity of the illness, and hence, prioritize the population treatment accordingly. We propose computer-based techniques that prove to be reliable as detectors for infected tissue in lung CT scans. The availability of such a method in today’s pandemic would help automate, prioritize, fasten, and broaden the treatment of COVID-19 patients globally.