Edge-and-Mask Integration-Driven Diffusion Models for Medical Image Segmentation

Abstract

Denoising diffusion probabilistic models (DDPMs) exhibit significant potential in the realm of medical image segmentation. Nevertheless, current DDPM implementations rely on original image features as conditional information, thus lacking the ability to specifically emphasize edge information, a critical aspect in addressing the primary challenge of segmentation. Furthermore, the necessary semantic features for conditioning the diffusion process lack effective alignment with the noise embedding. To address the above issues, we propose a novel edge-and-mask integration-driven diffusion model (EMidDiff). Specifically, 1) an edge-and-mask condition strategy is proposed for the segmentation diffusion model to effectively leverage rich semantic features, particularly the edge feature. 2) A novel co-attention guidance block is designed to align the segmentation map and condition features. The experimental results on brain tumor segmentation and optic-cup segmentation underscore the effectiveness of our approach, surpassing the performance of some state-of-the-art segmentation diffusion models.