mlsanityData to 14 Oct 2026

Exploring Robustness of Cortical Morphometry in the presence of white matter lesions, using Diffusion Models for Lesion Filling

arXiv 2503.20571 ↗First posted 26 Mar 2025eess.IV cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Cortical thickness measurements from magnetic resonance imaging, an important biomarker in many neurodegenerative and neurological disorders, are derived by many tools from an initial voxel-wise tissue segmentation. White matter (WM) hypointensities in T1-weighted imaging, such as those arising from multiple sclerosis or small vessel disease, are known to affect the output of brain segmentation methods and therefore bias cortical thickness measurements. These effects are well-documented among traditional brain segmentation tools but have not been studied extensively in tools based on deep-learning segmentations, which promise to be more robust. In this paper, we explore the potential of deep learning to enhance the accuracy and efficiency of cortical thickness measurement in the presence of WM lesions, using a high-quality lesion filling algorithm leveraging denoising diffusion networks. A pseudo-3D U-Net architecture trained on the OASIS dataset to generate synthetic healthy tissue, conditioned on binary lesion masks derived from the MSSEG dataset, allows realistic removal of white matter lesions in multiple sclerosis patients. By applying morphometry methods to patient images before and after lesion filling, we analysed robustness of global and regional cortical thickness measurements in the presence of white matter lesions. Methods based on a deep learning-based segmentation of the brain (Fastsurfer, DL+DiReCT, ANTsPyNet) exhibited greater robustness than those using classical segmentation methods (Freesurfer, ANTs).

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 17 of its 44 references that are in the map. First posted on arXiv 26 Mar 2025.

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17 of 44 references.

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Same direction, newest first

  1. 2024Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting21 Mar
  2. 2024Denoising Diffusion Models for Inpainting of Healthy Brain Tissue27 Feb
  3. 2023Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis19 Jul
  4. 2023Memory-Efficient 3D Denoising Diffusion Models for Medical Image Processing27 Mar

Other references · 13

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  4. 2020Denoising Diffusion Implicit Modelscited by 9,781
  5. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  6. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  7. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,430
  8. 2021Resolution-robust Large Mask Inpainting with Fourier Convolutionscited by 738
  9. 2018Free-Form Image Inpainting with Gated Convolutioncited by 594
  10. 2018Image Inpainting for Irregular Holes Using Partial Convolutionscited by 505
  11. 2023Efficient Diffusion Training via Min-SNR Weighting Strategycited by 246
  12. 2019FastSurfer -- A fast and accurate deep learning based neuroimaging pipelinecited by 112
1 more
  1. 2020High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsamplingcited by 79

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  2. 2024White Matter Geometry-Guided Score-Based Diffusion Model for Tissue Microstructure Imputation in Tractography Imaging
  3. 2025Modeling and Reversing Brain Lesions Using Diffusion Models
  4. 2024Discrepancy-based Diffusion Models for Lesion Detection in Brain MRI
  5. 2020Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning
  6. 2025State-of-the-Art Stroke Lesion Segmentation at 1/1000th of Parameters
  7. 2026Lesion-DDPM: Lesion-Enhanced 3D Diffusion for MS MRI Synthesis
  8. 2023Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI