Denoising Diffusion Probabilistic models have become increasingly popular due to their ability to offer probabilistic modeling and generate diverse outputs. This versatility inspired their adaptation for image segmentation, where multiple predictions of the model can produce segmentation results that not only achieve high quality but also capture the uncertainty inherent in the model. Here, powerful architectures were proposed for improving diffusion segmentation performance. However, there is a notable lack of analysis and discussions on the differences between diffusion segmentation and image generation, and thorough evaluations are missing that distinguish the improvements these architectures provide for segmentation in general from their benefit for diffusion segmentation specifically. In this work, we critically analyse and discuss how diffusion segmentation for medical images differs from diffusion image generation, with a particular focus on the training behavior. Furthermore, we conduct an assessment how proposed diffusion segmentation architectures perform when trained directly for segmentation. Lastly, we explore how different medical segmentation tasks influence the diffusion segmentation behavior and the diffusion process could be adapted accordingly. With these analyses, we aim to provide in-depth insights into the behavior of diffusion segmentation that allow for a better design and evaluation of diffusion segmentation methods in the future.

翻译：去噪扩散概率模型因其具备概率建模能力及生成多样化输出的特性而日益流行。这种多功能性启发人们将其应用于图像分割领域——模型的多次预测不仅能产生高质量的分割结果，还能捕获模型固有的不确定性。为此，研究者提出了多种强大架构以提升扩散分割性能。然而，目前鲜有对扩散分割与图像生成之间差异性的分析与讨论，也缺乏系统评估来区分这些架构对通用分割的改进与对扩散分割的特定增益。本研究重点分析并探讨医学图像扩散分割与扩散图像生成在训练行为上的差异。此外，我们评估了所提出的扩散分割架构直接用于分割训练时的性能表现。最后，我们探究不同医学分割任务如何影响扩散分割行为，以及如何相应调整扩散过程。通过这些分析，我们希望为扩散分割行为提供深度见解，从而推动未来扩散分割方法的设计与评估。