  In recent years, Denoising Diffusion Models have demonstrated remarkable
success in generating semantically valuable pixel-wise representations for
image generative modeling. In this study, we propose a novel end-to-end
framework, called Diff-UNet, for medical volumetric segmentation. Our approach
integrates the diffusion model into a standard U-shaped architecture to extract
semantic information from the input volume effectively, resulting in excellent
pixel-level representations for medical volumetric segmentation. To enhance the
robustness of the diffusion model's prediction results, we also introduce a
Step-Uncertainty based Fusion (SUF) module during inference to combine the
outputs of the diffusion models at each step. We evaluate our method on three
datasets, including multimodal brain tumors in MRI, liver tumors, and
multi-organ CT volumes, and demonstrate that Diff-UNet outperforms other
state-of-the-art methods significantly. Our experimental results also indicate
the universality and effectiveness of the proposed model. The proposed
framework has the potential to facilitate the accurate diagnosis and treatment
of medical conditions by enabling more precise segmentation of anatomical
structures. The codes of Diff-UNet are available at
https://github.com/ge-xing/Diff-UNet
