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

2024-03-25

arXiv.org

AI Summary

Conventional registration methods struggle with large anatomical variations across subpopulations (e.g., age- or disease-specific cohorts), while generative models often produce anatomically implausible artifacts. Method: We propose a latent diffusion model (LDM)-based framework that directly synthesizes nonlinear deformation fields—not images—thereby eliminating anatomical hallucination. Our approach integrates differentiable multi-scale registration with neighborhood consistency constraints to ensure structural plausibility and anatomical fidelity. Contribution/Results: Evaluated on 5,000 brain and whole-body MRI scans from UK Biobank, our method generates smooth, artifact-free, and highly generalizable population atlases. It significantly outperforms traditional registration and GAN-based approaches in both qualitative and quantitative assessments. The resulting deformation fields are interpretable, robust, and enable fine-grained analysis of anatomical differences—such as those associated with aging or pathological morphology—establishing a new paradigm for population-specific atlas construction.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Generates deformation fields for conditional atlases using diffusion models
• Handles large anatomical variations better than registration-based methods
• Avoids hallucinations and ensures structural integrity in atlas generation

Innovation

• Latent diffusion models generate deformation fields
• Regularize fields using image neighborhoods
• Transform general atlas to sub-population atlas