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

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.

RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation

“…From previous studies, we can observe DL methods can have better performance than traditional ML methods. As mentioned before, DL is a subfield of ML (see Figure 1), but DL powers itself by using a human-like artificial deep neural network to learn and make decisions by itself from given data (Saood and Hatem, 2021).…”

Robust chest CT image segmentation of COVID-19 lung infection based on limited data

“…The 3D transformation of these architectures and the integration into our pipeline would be an interesting experiment to evaluate improvement possibilities. Other high-performance 2D approaches like Saood et al [ 37 ] and Pei et al [ 38 ] were difficult to compare due to these models are purely trained and evaluated on 2D slices with COVID-19 presence [ 66 ].…”

“…Nonetheless, multiple approaches try to solve these problems with different methods. The most popular network models for COVID-19 segmentation are variants of the U-Net which achieved reasonable performance on sufficiently sized 2D datasets [ 5 , 10 , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] ]. In order to compensate limited dataset sizes, more attention has been drawn to semi-supervised learning pipelines [ 10 , 41 , 42 ].…”

PCXRNet: Condense attention block and Multiconvolution spatial attention block for Pneumonia Chest X-Ray detection

“…Therefore, CNNs are usually viewed as a general feature extractor with no need for prior knowledge and human intervention. CNNs have achieved remarkable performance on many tasks, such as classification [10], object detection [11] and semantic segmentation [12]. Deep learning is also used to detect lung diseases with the release of medical imaging datasets.…”