DBEF-Net: Diffusion-Based Boundary-Enhanced Fusion Network for medical image segmentation

Medical image segmentation aims to locate lesions within a given image to assist doctors in diagnosis and treatment, playing a crucial role in improving patient outcomes. Recently, the Denoising Diffusion Probabilistic Model (DDPM) has demonstrated its advantages in various generative tasks. Building upon these advantages, we propose DBEF-Net: Diffusion-Based Boundary-Enhanced Fusion Network for Medical Image Segmentation. When applying diffusion models to image segmentation, the challenge of inconsistent semantic features and noise embedding needs to be addressed. To overcome this issue, we introduce the Group Attention Fusion Module (GAFM), which merges image features and noise features in groups to better utilize semantic information and noise characteristics in the diffusion model. Additionally, we design the Boundary-Attentive Fusion Module (BAFM) to incorporate boundary priors into the diffusion model to enhance sensitivity to fuzzy boundaries. We also introduce the Spatial Context Fusion Module (SCFM) to fully exploit multi-scale information in the encoder and decoder. We conduct experiments on four datasets with DBEF-Net, comparing it with other methods. Encouragingly, our approach shows significant performance improvement while maintaining novelty.