A Multi‐Scale Boundary‐Enhanced Denoising Diffusion Network for Medical Image Segmentation

ABSTRACT Automatic and accurate medical image segmentation (MIS) can assist doctors identify regions of interest (ROIs) more efficiently, and provide more reliable diagnostic information and treatment options. In recent years, the denoising diffusion model, known for its excellent detail expression ability and good generalization performance, has demonstrated promising effect in MIS. The existing diffusion‐based segmentation networks typically take the original images as the conditional information, and ignore the ambiguity of the ROIs' boundaries, resulting in inconsistent boundary predictions and inaccurate segmentation results. The variability in the size and shape of ROIs poses additional challenges when applying diffusion models to MIS. To solve these problems, we propose a multi‐scale boundary‐enhanced diffusion segmentation network (MBDS‐Net) for MIS to improve the accuracy of boundary segmentation. Specifically, we design a multi‐scale boundary‐aware enhancement (MBE) module to enhance the boundary restoration ability of the ROIs of different scales and shapes. Besides, we propose an attention denoising residual (ADR) module that focuses on extracting key features during the progressive denoising process, reducing the impact of noise on segmentation and enhancing the robustness of the model. Furthermore, we adopt deep supervision in the decoder to enhance the training convergence and feature discriminability of the diffusion model. We conduct experiments on three public datasets and compare our model to the existing advanced segmentation models to demonstrate its superiority in MIS. The code is available at https://github.com/FionaYeager/MBDS‐Net .