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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

TL;DR

This work critically analyse and discuss how diffusion segmentation for medical images differs from diffusion image generation, with a particular focus on the training behavior.

Abstract

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.

Analysing Diffusion Segmentation for Medical Images

TL;DR

This work critically analyse and discuss how diffusion segmentation for medical images differs from diffusion image generation, with a particular focus on the training behavior.

Abstract

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.
Paper Structure (11 sections, 2 equations, 4 figures, 1 table)

This paper contains 11 sections, 2 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background on Diffusion Models
  3. Datasets and Experiments
  4. Datasets
  5. Experiments
  6. Evaluation
  7. Feed-forward Segmentation Performance vs. Diffusion Segmentation
  8. Error and Loss Behaviour of Diffusion Segmentation over Different Timesteps
  9. Dataset Fingerprints
  10. Conclusion
  11. Acknowledgement

Figures (4)

  • Figure 1: Comparision of segmentation results for MoNuSeg with SegDiff (upper row) and ISIC16 with MedSegDiff (lower row), when the networks were trained for diffusion segmentation or for feed-forward segmentation with random noises as $x_{t}$ input.
  • Figure 2: Forward process for images (upper row) and segmentation mask (lower row), on the example of the HER2 dataset, with 1000 diffusion steps and linear beta schedule.
  • Figure 3: Smoothed curves of the mask prediction error (left), loss for the task of diffusion segmentation (middle) and image generation (right) for EnsemDiff on HER2. Models were picked after 75% of the total number of training steps.
  • Figure 4: Smoothed mask prediction error for the three datasets for each architecture (dottet) and averaged (solid).