HiDiffSeg: A hierarchical diffusion model for blood vessel segmentation in retinal fundus images

Abstract

In medical diagnosis, achieving accurate segmentation of fundus vessels is crucial for understanding and identifying various anatomical structures. However, traditional algorithms often face challenges in achieving precise segmentation due to the poor quality of fundus images and the complex branching structure of vessels. To address this issue, we propose a novel approach called HiDiffSeg, which is a coarse-to-fine hierarchical diffusion model for blood vessel segmentation. Our method integrates an enhanced processing coarse-to-fine strategy for retinal fundus images into the denoising process of the diffusion model. The diffusion model is divided into two modules: the dual-guidance module (DGM) and the refinement-guidance module (RGM). The DGM takes the vascular skeleton image and the initial denoised vessel segmentation image generated by the vascular image enhancement model as conditions. Meanwhile, the RGM uses the fundus image as a condition to obtain more accurate results through iterative refinement. To emphasize the significance of vessel edges, we introduce a vessel enhancement module. By taking the original image as input, we generate a pixel-wise edge attention map, assigning greater importance to corresponding edge pixels. To the best of our knowledge, this is the first time a hierarchical diffusion model has been applied to fundus vessel segmentation. We demonstrate the accuracy of our method on three publicly available fundus retinal datasets (i.e., DRIVE, STARE, and CHASE_DB1) using evaluation metrics and compare it with eleven state-of-the-art fundus vessel segmentation methods.