  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
