Medical image segmentation is a challenging task, made more difficult by many datasets' limited size and annotations. Denoising diffusion probabilistic models (DDPM) have recently shown promise in modelling the distribution of natural images and were successfully applied to various medical imaging tasks. This work focuses on semi-supervised image segmentation using diffusion models, particularly addressing domain generalisation. Firstly, we demonstrate that smaller diffusion steps generate latent representations that are more robust for downstream tasks than larger steps. Secondly, we use this insight to propose an improved esembling scheme that leverages information-dense small steps and the regularising effect of larger steps to generate predictions. Our model shows significantly better performance in domain-shifted settings while retaining competitive performance in-domain. Overall, this work highlights the potential of DDPMs for semi-supervised medical image segmentation and provides insights into optimising their performance under domain shift.

翻译：医学图像分割是一项具有挑战性的任务，而许多数据集规模有限且标注不足使得这一任务更加困难。近年来，去噪扩散概率模型（DDPM）在建模自然图像分布方面展现出潜力，并已成功应用于各类医学成像任务。本研究聚焦于基于扩散模型的半监督图像分割，特别关注域泛化问题。首先，我们证明相较于大步长，较小扩散步长生成的潜在表征对下游任务具有更强的鲁棒性。其次，基于这一发现，我们提出一种改进的集成方案，该方案利用信息密集的小步长与大步长的正则化效应生成预测结果。我们的模型在域偏移场景下展现出显著更优的性能，同时保持域内性能具有竞争力。总体而言，本研究揭示了DDPM在医学图像半监督分割中的潜力，并为优化其在域偏移条件下的性能提供了启发性见解。