mlsanityData to 14 Oct 2026

Image-Conditioned Diffusion Models for Quality Assurance of Organ-at-Risk Segmentations in Radiotherapy

arXiv 2608.23432 ↗First posted 24 Aug 2026cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Accurate organ-at-risk segmentation is essential for radiotherapy planning, but reviewing segmentations is time-consuming and subjective. We investigate normative modelling for segmentation error detection in head-and-neck CT, comparing a VAE framework with an image-conditioned segmentation diffusion model. Models were evaluated on RADCURE brainstem and spinal cord segmentations using simulated boundary and width perturbations. Error detection was assessed using the Dice similarity coefficient and the Distance to Agreement (DTA) between the input and reconstructed segmentations. While both models detected some simulated errors, regional DTA showed that the diffusion model localised subtle boundary errors more consistently. These results support image-conditioned diffusion reconstruction as a promising framework for localised, anatomy-aware segmentation QA.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 5 of its 18 references that are in the map. First posted on arXiv 24 Aug 2026.

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5 of 18 references.

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  1. 2023BerDiff: Conditional Bernoulli Diffusion Model for Medical Image Segmentation10 Apr

Other references · 4

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2020Denoising Diffusion Implicit Modelscited by 9,781
  3. 2022TotalSegmentator: robust segmentation of 104 anatomical structures in CT imagescited by 613
  4. 2020Deep Generative Model-based Quality Control for Cardiac MRI Segmentationcited by 22

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  8. 2021Diffusion Models for Implicit Image Segmentation Ensembles