MS-UNet: A Novel Multi-scale U-shaped Network for COVID-19 CT Image Segmentation

The U-Net network has its own powerful capabilities in medical image segmentation tasks, yet still it is a challenging task to make U-Net accurately segment the infected lesions of COVID-19 CT images because these lesion areas are usually irregular in shape, various in size, and blurry in boundaries. In this paper, a novel multiscale U-shaped network based on U-Net for accurate segmentation of lesion regions in COVID-19 CT images is proposed. First, we generate two auxiliary scale features (fi0.5, fi1.5) based on the main scale feature (fi1.0) through zoom strategy. Secondly, we design the Scale Integration Module (SIM), which is capable of filtering and aggregating scale-specific features and can fully exploit multi-scale semantic information. Thirdly, the hierarchical mixed module (HMM) has successfully substituted for the down-up aggregation process of the U-Net network, which further enhances the mixed scale features. On the dataset COVID-19-CT829, compared with the recent COVID-19 segmentation model, hiformer, the Dice, Sen and F-measure of our network have increased by 2.24%, 2.83%, 3.14%, respectively; on the dataset COVID-19-CT100, the Dice, Sen and F-measure of our network have increased by 2.91%, 3.72%, 2.42%, respectively. Moreover, we have validated the generalizability and portability of our network on other medical datasets (Polyp segmentation dataset: CVC-612 and kvasir), and our network has also achieved superior results of COVID-19 CT image segmentation.