  Coronavirus Disease 2019 (COVID-19) has caused great casualties and becomes
almost the most urgent public health events worldwide. Computed tomography (CT)
is a significant screening tool for COVID-19 infection, and automated
segmentation of lung infection in COVID-19 CT images will greatly assist
diagnosis and health care of patients. However, accurate and automatic
segmentation of COVID-19 lung infections remains to be challenging. In this
paper we propose a dilated dual attention U-Net (D2A U-Net) for COVID-19 lesion
segmentation in CT slices based on dilated convolution and a novel dual
attention mechanism to address the issues above. We introduce a dilated
convolution module in model decoder to achieve large receptive field, which
refines decoding process and contributes to segmentation accuracy. Also, we
present a dual attention mechanism composed of two attention modules which are
inserted to skip connection and model decoder respectively. The dual attention
mechanism is utilized to refine feature maps and reduce semantic gap between
different levels of the model. The proposed method has been evaluated on
open-source dataset and outperforms cutting edges methods in semantic
segmentation. Our proposed D2A U-Net with pretrained encoder achieves a Dice
score of 0.7298 and recall score of 0.7071. Besides, we also build a simplified
D2A U-Net without pretrained encoder to provide a fair comparison with other
models trained from scratch, which still outperforms popular U-Net family
models with a Dice score of 0.7047 and recall score of 0.6626. Our experiment
results have shown that by introducing dilated convolution and dual attention
mechanism, the number of false positives is significantly reduced, which
improves sensitivity to COVID-19 lesions and subsequently brings significant
increase to Dice score.
