SMA-Net: Sobel Operator Combined with Multi-attention Networks for COVID-19 Lesion Segmentation

Abstract

Coronavirus disease 2019 (COVID-19) has been spreading since late 2019, leading the world into a serious health crisis. To control the spread rate of infection, identifying patients accurately and quickly is the most crucial step. Computed tomography (CT) images of the chest are an important basis for diagnosing COVID-19. They also allow doctors to understand the details of the lung infection. However, manual segmentation of infected areas in CT images is time-consuming and laborious. With its excellent feature extraction capabilities, deep learning-based method has been widely used for automatic lesion segmentation of COVID-19 CT images. But, the segmentation accuracy of these methods is still limited. To effectively quantify the severity of lung infections, we propose a Sobel operator combined with Multi-Attention networks for COVID-19 lesion segmentation (SMA-Net). In our SMA-Net, an edge feature fusion module uses Sobel operator to add edge detail information to the input image. To guide the network to focus on key regions, the SMA-Net introduces a self-attentive channel attention mechanism and a spatial linear attention mechanism. In addition, Tversky loss function is adopted for the segmentation network for small size of lesions. Comparative experiments on COVID-19 public datasets show that the average Dice similarity coefficient (DSC) and joint intersection over Union (IOU) of proposed SMA-Net are 86.1% and 77.8%, respectively, which are better than most existing neural networks used for COVID-19 lesion segmentation.