2-D general network based on channel-space attention for medical image segmentation

General and effective medical image segmentation methods achieve pixel-level accurate segmentation of lesions or organs. Existing 2-D networks have difficulty in exploiting complementary information among modalities on multi-modal segmentation tasks. This study proposes a multi-scale general medical image segmentation 2-D network (MCS-Net) based on CNN and Transformer. Specifically, a channel-space attention module is designed to independently group feature maps and calculate channel attention weights within each group, which successfully fuses channel information among different scales. We also introduce a multi-scale convolution module to acquire more abundant multi-receptive field features, which is beneficial for extracting multi-scale features and obtaining fine-grained local information. Extensive experiments on typical single-modal and multi-modal image segmentation tasks demonstrate the accuracy and effectiveness of MCS-Net and show that our approach is significantly superior to current state-of-the-art methods.