D2A U-Net: Automatic segmentation of COVID-19 CT slices based on dual attention and hybrid dilated convolution

Coronavirus Disease 2019 (COVID-19) has become one of the most urgent public health events worldwide due to its high infectivity and mortality. Computed tomography (CT) is a significant screening tool for COVID-19 infection, and automatic segmentation of lung infection in COVID-19 CT images can assist diagnosis and health care of patients. However, accurate and automatic segmentation of COVID-19 lung infections is faced with a few challenges, including blurred edges of infection and relatively low sensitivity. To address the issues above, a novel dilated dual attention U-Net based on the dual attention strategy and hybrid dilated convolutions, namely D2A U-Net, is proposed for COVID-19 lesion segmentation in CT slices. In our D2A U-Net, the dual attention strategy composed of two attention modules is utilized to refine feature maps and reduce the semantic gap between different levels of feature maps. Moreover, the hybrid dilated convolutions are introduced to the model decoder to achieve larger receptive fields, which refines the decoding process. The proposed method is evaluated on an open-source dataset and achieves a Dice score of 0.7298 and recall score of 0.7071, which outperforms the popular cutting-edge methods in the semantic segmentation. The proposed network is expected to be a potential AI-based approach used for the diagnosis and prognosis of COVID-19 patients.

Automated summary

The D2A U-Net proposed in this study is an innovative approach based on dual attention strategy and hybrid dilated convolutions for segmentation of COVID-19 lung infections. Experimental results show that the method achieved good scores on an open dataset, and is expected to be a potential artificial intelligence approach for the diagnosis and prognosis of COVID-19 patients.