Diff-Def: Diffusion-Generated Deformation Fields for Conditional Atlases

• URL: http://arxiv.org/abs/2403.16776v1
• Date: Mon, 25 Mar 2024 13:52:48 GMT
• Title: Diff-Def: Diffusion-Generated Deformation Fields for Conditional Atlases
• Authors: Sophie Starck, Vasiliki Sideri-Lampretsa, Bernhard Kainz, Martin Menten, Tamara Mueller, Daniel Rueckert,
• Abstract summary: Conditional atlases allow for the investigation of fine-grained anatomical differences. We use latent diffusion models to generate deformation fields, which transform a general population atlas into a specific sub-population. We compare our method to several state-of-the-art atlas generation methods in experiments using 5000 brain as well as whole-body MR images from UK Biobank.
• Score: 13.440406411539987
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Anatomical atlases are widely used for population analysis. Conditional atlases target a particular sub-population defined via certain conditions (e.g. demographics or pathologies) and allow for the investigation of fine-grained anatomical differences - such as morphological changes correlated with age. Existing approaches use either registration-based methods that are unable to handle large anatomical variations or generative models, which can suffer from training instabilities and hallucinations. To overcome these limitations, we use latent diffusion models to generate deformation fields, which transform a general population atlas into one representing a specific sub-population. By generating a deformation field and registering the conditional atlas to a neighbourhood of images, we ensure structural plausibility and avoid hallucinations, which can occur during direct image synthesis. We compare our method to several state-of-the-art atlas generation methods in experiments using 5000 brain as well as whole-body MR images from UK Biobank. Our method generates highly realistic atlases with smooth transformations and high anatomical fidelity, outperforming the baselines.

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