Diffusing the Blind Spot: Uterine MRI Synthesis with Diffusion Models

TL;DR

This paper introduces a novel diffusion-based framework for synthesizing high-quality, anatomically accurate uterine MRI images, addressing data scarcity and privacy issues in gynaecological imaging, and improving diagnostic model training.

Contribution

The paper presents a new diffusion model framework combining multiple diffusion techniques for realistic uterine MRI synthesis, with extensive evaluation and dataset release.

Findings

Synthetic images are highly realistic and anatomically coherent.

Generated data improves diagnostic classification accuracy.

Expert evaluation confirms clinical realism.

Abstract

Despite significant progress in generative modelling, existing diffusion models often struggle to produce anatomically precise female pelvic images, limiting their application in gynaecological imaging, where data scarcity and patient privacy concerns are critical. To overcome these barriers, we introduce a novel diffusion-based framework for uterine MRI synthesis, integrating both unconditional and conditioned Denoising Diffusion Probabilistic Models (DDPMs) and Latent Diffusion Models (LDMs) in 2D and 3D. Our approach generates anatomically coherent, high fidelity synthetic images that closely mimic real scans and provide valuable resources for training robust diagnostic models. We evaluate generative quality using advanced perceptual and distributional metrics, benchmarking against standard reconstruction methods, and demonstrate substantial gains in diagnostic accuracy on a key classification task. A blinded expert evaluation further validates the clinical realism of our synthetic images. We release our models with privacy safeguards and a comprehensive synthetic uterine MRI dataset to support reproducible research and advance equitable AI in gynaecology.