MCA-Unet: A multiscale context aggregation U-Net for the segmentation of COVID-19 lesions from CT images

Abstract

The pandemic of coronavirus disease (COVID-19) caused the world to face an existential health crisis. COVID-19 lesions segmentation from CT images is nowadays an essential step to assess the severity of the disease and the amount of damage to the lungs. Deep learning has brought about a breakthrough in medical image segmentation where U-Net is the most prominent deep network. However, in this study, we argue that its architecture still lacks in certain aspects. First, there is an incompatibility in the U-Net skip connection between the encoder and decoder features which adversely affects the final prediction. Second, it lacks capturing multiscale context information and ignores the contribution of all semantic information through the segmentation process. Therefore, we propose a model named MCA-Unet, a novel multiscale deep learning segmentation model, which proposes some modifications to improve upon the U-Net model. MCA-Unet is integrated with a multiscale context aggregation module which is constituted of two blocks; a context embedding block (CEB) and a cascaded dilated convolution block (CDCB). The CEB aims at reducing the semantic gap between the concatenated features along the U-Net skip connections, it enriches the low-level encoder features with rich semantics inherited from the subsequent higher-level features, to reduce the semantic gap between the low-processed encoder features and the highly-processed decoder features, thus ensuring effectual concatenation. The CDCB is integrated to address the variability in shape and size of the COVID-19 lesions, it captures global context information by gradually expanding the receptive field, then operates reversely to capture the small fine details that might be scattered by enlarging the receptive field. To validate the robustness of our model, we tested it on a publicly available dataset of 1705 axial CT images with different types of COVID-19 infection. Experimental results show that MCA-Unet has attained a remarkable gain in performance in comparison with the basic U-Net and its variant. It achieved high performance using different evaluation metrics showing 88.6% Dice similarity coefficient, 85.4% Jaccard index, and 93.5% F-score measure. This outperformance shows great potential to help physicians during their examination and improve the clinical workflow.