Analysing Diffusion Segmentation for Medical Images
Mathias Öttl Siyuan Mei Frauke Wilm Jana Steenpass Matthias Rübner Arndt Hartmann Matthias Beckmann Peter Fasching Andreas Maier Ramona Erber Katharina Breininger
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.