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Investigating the Role of Bilateral Symmetry for Inpainting Brain MRI

arXiv 2504.10039 ↗First posted 14 Apr 2025cs.CV cs.LGcited by 0

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Abstract (arXiv)

Inpainting has recently emerged as a valuable and interesting technology to employ in the analysis of medical imaging data, in particular brain MRI. A wide variety of methodologies for inpainting MRI have been proposed and demonstrated on tasks including anomaly detection. In this work we investigate the statistical relationship between inpainted brain structures and the amount of subject-specific conditioning information, i.e. the other areas of the image that are masked. In particular, we analyse the distribution of inpainting results when masking additional regions of the image, specifically the contra-lateral structure. This allows us to elucidate where in the brain the model is drawing information from, and in particular, what is the importance of hemispherical symmetry? Our experiments interrogate a diffusion inpainting model through analysing the inpainting of subcortical brain structures based on intensity and estimated area change. We demonstrate that some structures show a strong influence of symmetry in the conditioning of the inpainting process.

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References · 16

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
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  9. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,419
  10. 2018Free-Form Image Inpainting with Gated Convolutioncited by 594
  11. 2018Image Inpainting for Irregular Holes Using Partial Convolutionscited by 505
  12. 2019Pluralistic Image Completioncited by 162
4 more
  1. 2021Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAEcited by 106
  2. 2023Unsupervised 3D out-of-distribution detection with latent diffusion modelscited by 22
  3. 2023Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detectioncited by 18
  4. 2018Adversarial Inpainting of Medical Image Modalitiescited by 16

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