Multi-task semantic segmentation of CT images for COVID-19 infections using DeepLabV3+ based on dilated residual network

Abstract

COVID-19 is a deadly outbreak that has been declared a public health emergency of international concern. The massive damage of the disease to public health, social life, and the global economy increases the importance of alternative rapid diagnosis and follow-up methods. RT-PCR assay, which is considered the gold standard in diagnosing the disease, is complicated, expensive, time-consuming, prone to contamination, and may give false-negative results. These drawbacks reinforce the trend toward medical imaging techniques such as computed tomography (CT). Typical visual signs such as ground-glass opacity (GGO) and consolidation of CT images allow for quantitative assessment of the disease. In this context, it is aimed at the segmentation of the infected lung CT images with the residual network-based DeepLabV3+, which is a redesigned convolutional neural network (CNN) model. In order to evaluate the robustness of the proposed model, three different segmentation tasks as Task-1, Task-2, and Task-3 were applied. Task-1 represents binary segmentation as lung (infected and non-infected tissues) and background. Task-2 represents multi-class segmentation as lung (non-infected tissue), COVID (GGO, consolidation, and pleural effusion irregularities are gathered under a single roof), and background. Finally, the segmentation in which each lesion type is considered as a separate class is defined as Task-3. COVID-19 imaging data for each segmentation task consists of 100 CT single-slice scans from over 40 diagnosed patients. The performance of the model was evaluated using Dice similarity coefficient (DSC), intersection over union (IoU), sensitivity, specificity, and accuracy by performing five-fold cross-validation. The average DSC performance for three different segmentation tasks was obtained as 0.98, 0.858, and 0.616, respectively. The experimental results demonstrate that the proposed method has robust performance and great potential in evaluating COVID-19 infection.