EAswin-unet: Segmenting CT images of COVID-19 with edge-fusion attention

Abstract

In this study, we present a novel deep learning network model, EAswin-unet, for accurately segmenting COVID-19 lesions in chest CT images. Our model adopts a fusion strategy that combines edge-weighted attention features and Swin-unet pure attention model features to increase the weight of edge recognition and improve segmentation accuracy. Compared to the inf-net network, our model achieved a significant increase of 0.055 in Dice score and 0.024 in sensitivity, while reducing the number of model parameters by 11770. To further improve the accuracy of our model, we also employed a hybrid semi-supervised algorithm strategy to use labeled data to correct model training and extract non-edge local information. Our proposed model was evaluated on a dataset of 100 labeled chest CT images from COVID-19 patients and 1600 unlabeled data, achieving a Dice similarity coefficient (DSC) of 0.737, sensitivity of 0.716, and specificity of 0.929, surpassing state-of-the-art methods. These results demonstrate the effectiveness of our proposed model in accurately segmenting COVID-19 lesions in chest CT images, providing an important tool for radiologists and clinicians in the diagnosis and treatment of COVID-19 patients.