MFSegDiff: A Multi-Frequency Diffusion Model for Medical Image Segmentation

Medical image segmentation accurately identifies and delineates diagnostic regions, which is a crucial step in the early detection and accurate diagnosis of diseases. Diffusion models have demonstrated remarkable performance in preserving image details and structures by gradually adding noise followed by a reverse denoising process, making them widely explored in image segmentation tasks. In this study, we propose a novel approach for medical image segmentation utilizing diffusion models, termed MFSegDiff, which frames the segmentation task as an iterative denoising process. To address the inconsistency between image semantic features and noise embeddings, we introduce a Cross-Attention Alignment Module(CAAM). This module enhances the original image features and integrates noise and semantic information into the network through a linear attention mechanism. Additionally, we employ a Global-Local Multi-Frequency Module (GLMFM) to extract global contextual information from the spatial to the frequency domain by integrating multi-frequency features with local features. We evaluate the proposed on three datasets, including ISIC-2017, ISIC-2018, and ROSE. Results demonstrate strong generalization capabilities and achieve state-of-the-art segmentation performance, highlighting significant potential for clinical applications.