Abstract: Medical image segmentation has important application value in clinical diagnosis,treatment and pathological analysis.In recent years,denoising diffusion models have achieved remarkable success in image segmentation modeling,which can better capture complex structure and detail information in images.However,most of the methods using the denoising diffusion model for medical image segmentation ignore the boundary uncertainty and region ambiguity of the segmentation target,resulting in the instability and inaccuracy of the final segmentation results.In order to solve this problem,a medical image diffusion fusion network segmentation model driven based on fuzzy logic(FDiff-Fusion) is proposed.By integrating the denoising diffusion model into the classical U-Net network,this model can effectively extract rich semantic information from inputting medical images.Since the boundary uncertainty and region blurring of medical image segmentation are common,a fuzzy learning module is designed on the jump path of U-Net network.The module sets several fuzzy membership functions for the input encoded features to describe the similarity degree between the feature points,and applies fuzzy rules to the fuzzy membership functions,thus enhancing the modeling ability of the model to the uncertain boundary and fuzzy region.In addition,in order to improve the accuracy and robustness of the model segmentation results,a method based on iterative attention feature fusion is introduced in the test phase,which adds local context information to the global context information in the attention module to fuse the prediction results of each denoising time step.Experimental results show that compared with existing advanced segmentation networks,the average Dice score and the average HD95 distance obtained by FDiff-Fusion on BRATS 2020 brain tumor dataset are 84.16% and 2.473mm,respectively.The mean Dice score and the mean HD95 distance obtained on BTCV abdominal multi-organ dataset are 83.41% and 7.98mm,respectively,showing good segmentation performance.

Key words: Denoising diffusion model, U-Net network, Medical image segmentation, Fuzzy learning, Iterative attention feature fusion