DC-Net: Dual Context Network for 2D Medical Image Segmentation

Medical image segmentation is essential for disease diagnosis analysis. There are many variants of U-Net that are based on attention mechanism and dense connections have made progress. However, CNN-based U-Net lacks the ability to capture the global context, and the context information of different scales is not effectively integrated. These limitations lead to the loss of potential context information. In this work, we propose a Dual Context Network (DC-Net) to aggregate global context and fuse multi-scale context for 2D medical image segmentation. In order to aggregate the global context, we present the Global Context Transformer Encoder (GCTE), which reshapes the original image and the multi-scale feature maps into a sequence of image patches, and combines the advantages of Transformer Encoder on global context aggregation to improve the performance of encoder. For the fusion of multi-scale context, we propose the Adaptive Context Fusion Module (ACFM) to adaptively fuse context information by learning Adaptive Spatial Weights and Adaptive Channel Weights to improve the performance of decoder. We apply our DC-Net with GCTE and ACFM to skin lesion segmentation and cell contour segmentation tasks, experimental results show that our method can outperform other advanced methods and get state-of-the-art performance.