NextSeg: Automatic COVID-19 Lung Infection Segmentation from CT Images Based on Next-ViT

The spread of COVID-19 has brought great disaster to the world, and automatic segmentation of the infected region can help doctors make a quick diagnosis and reduce workload. However, automatic segmentation of infected regions in computed tomography (CT) images still faces many challenges. The most central problem at the moment is that when capturing low-frequency signals and high-frequency signals, the use of Transformer blocks for capturing low-frequency signals may deteriorate the high-frequency signals (local texture information) to some extent. For this reason, in this paper, we propose a new network for automatic segmentation of COVID-19 lung infections from CT images, called NextSeg. NextSeg follows an encoder-decoder architecture, where the encoder part uses NCB and NTB convolutional attention modules to extract better features while reducing the deterioration of texture profile information. The decoder part uses an attention fusion module to learn the weight relationship between features and retain more important feature information. In addition to the difficulty of extracting the boundary details of the COVID-19 infected region as well as the loss of details the deeper the network is, we designed a new hybrid strategy. Traditional and state-of-the-art approaches to the COVID-19 segmentation domain are compared. Extensive experiments show that our proposed framework outperforms existing competitors in both qualitative and quantitative results.