Diffusion models have demonstrated their effectiveness across various generative tasks. However, when applied to medical image segmentation, these models encounter several challenges, including significant resource and time requirements. They also necessitate a multi-step reverse process and multiple samples to produce reliable predictions. To address these challenges, we introduce the first latent diffusion segmentation model, named SDSeg, built upon stable diffusion (SD). SDSeg incorporates a straightforward latent estimation strategy to facilitate a single-step reverse process and utilizes latent fusion concatenation to remove the necessity for multiple samples. Extensive experiments indicate that SDSeg surpasses existing state-of-the-art methods on five benchmark datasets featuring diverse imaging modalities. Remarkably, SDSeg is capable of generating stable predictions with a solitary reverse step and sample, epitomizing the model's stability as implied by its name. The code is available at https://github.com/lin-tianyu/Stable-Diffusion-Seg

翻译：扩散模型已在多种生成任务中证明了其有效性。然而，当应用于医学图像分割时，这些模型面临若干挑战，包括显著的资源与时间需求。它们还需要多步逆向过程及多个样本来产生可靠的预测。为解决这些挑战，我们提出了首个基于稳定扩散（SD）构建的潜在扩散分割模型，命名为SDSeg。SDSeg采用了一种简单的潜在估计策略以实现单步逆向过程，并利用潜在融合拼接技术消除了对多个样本的需求。大量实验表明，SDSeg在五个包含多种成像模态的基准数据集上超越了现有的最先进方法。值得注意的是，SDSeg能够仅通过单一的逆向步骤和样本生成稳定的预测，这完美体现了其名称所暗示的模型稳定性。代码可在 https://github.com/lin-tianyu/Stable-Diffusion-Seg 获取。