EGLA-Net: Edge Guided with Lesion Aware Network for Medical image segmentation

Medical image segmentation plays a crucial role in diagnosis analysis and disease treatment. However, the boundaries of most lesion areas are blurred, and there are significant differences between the shape, size, and appearance of different lesion areas, which pose a challenge to many methods. To solve these problems, we propose an edge guided with lesion aware network (EGLA-Net). Specifically, we connect an edge attention (EA) module at each stage of the encoder to preserve more local edge features. And we design multiple global pyramidal guidance (GPG) modules to provide different levels of global information for the decoder. Further, we introduce a dynamic kernel generation (KG) and kernel update (KU) mechanism that utilizes continuously updated kernel parameters to learn and mine distinguishable regional features, resolving differences in shape, size, and appearance of different diseased regions. Extensive experiments show that the EGLA-Net can achieve superior segmentation performance.