Anatomically-conditioned Latent Diffusion Model for Data-Efficient Few-Shot Cross-Domain 3D Glioma MRI Synthesis

2026-06-24

AI Summary

This study addresses the challenge of classifying diffuse gliomas across multi-center MRI datasets, where domain shift and scarce annotations hinder performance. The authors propose an anatomy-guided latent diffusion model within a two-stage framework: first, a 3D variational autoencoder learns anatomical priors from the source domain; then, a tumor mask-conditioned latent diffusion model, steered by ControlNet, synthesizes structurally coherent and boundary-sharp 3D glioma MRIs using only 16 target-domain samples. This work is the first to integrate anatomical priors with ControlNet-guided diffusion for data-efficient, few-shot cross-domain medical image synthesis. Experiments demonstrate that the method achieves a Fréchet Inception Distance (FID) of 85.40 and a downstream classification AUC of 0.987 under extreme data scarcity, significantly outperforming GAN-based baselines.

Abstract

Accurate classification of diffuse gliomas is often hindered by domain shifts across centers and a lack of large, annotated datasets. We propose the Anatomically-conditioned Latent Diffusion Model (ALDM), a novel framework for data-efficient, few-shot 3D volumetric MRI synthesis. ALDM utilizes a two-stage approach: a 3D variational autoencoder learns anatomical priors from a data-rich source domain, while a conditional latent diffusion model, guided by tumor masks via a ControlNet, generates structurally coherent volumes for a data-scarce target domain. Evaluated in an extreme few-shot setting with only 16 target images, ALDM outperformed GAN and hybrid baselines, achieving a superior Frechet Inception Distance (FID) of 85.40 and a downstream classification AUC of 0.987. Qualitative results confirm that the model preserves sharp pathology boundaries and cross-modal consistency, with visual fidelity improving progressively during training. By capturing essential diagnostic features, ALDM provides a robust tool for clinical data augmentation in low-resource settings. Our implementation is available at https://github.com/Analytics-Everywhere-Lab/anatomically-conditioned-LDM.

Problem

• domain shift
• few-shot learning
• 3D MRI synthesis
• data scarcity
• glioma classification

Innovation

• Latent Diffusion Model
• Few-Shot Learning
• 3D MRI Synthesis
• Anatomical Priors
• ControlNet