Graph-Convolutional-Beta-VAE for Synthetic Abdominal Aorta Aneurysm Generation

Abstract

Synthetic data generation plays a crucial role in medical research by mitigating privacy concerns and enabling large-scale patient data analysis. This study presents a beta-Variational Autoencoder Graph Convolutional Neural Network framework for generating synthetic Abdominal Aorta Aneurysms (AAA). Using a small real-world dataset, the approach extracts key anatomical features and captures complex statistical relationships within a compact disentangled latent space. To address data limitations, low-impact data augmentation based on Procrustes analysis was employed, preserving anatomical integrity. The generation strategies, both deterministic and stochastic, manage to enhance data diversity while ensuring realism. Compared to PCA-based approaches, the model performs more robustly on unseen data by capturing complex, nonlinear anatomical variations.

Please cite this paper as:

@article{fabbri2025graph,
title={Graph-Convolutional-Beta-VAE for Synthetic Abdominal Aorta Aneurysm Generation},
author={Fabbri, Francesco and Scarpolini, Martino Andrea and Iollo, Angelo and Viola, Francesco and Tudisco, Francesco},
journal={arXiv preprint arXiv:2506.13628},
year={2025}
}

Keywords: synthetic data generation medical AI variational autoencoder graph convolutional networks abdominal aorta aneurysm beta-VAE medical imaging data augmentation deep learning