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

16 June 2025
Francesco Fabbri
Martino Andrea Scarpolini
Angelo Iollo
Francesco Viola
Francesco Tudisco
    MedIm
ArXiv (abs)PDFHTML
Main:16 Pages
14 Figures
Bibliography:3 Pages
1 Tables
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, our 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, our model performs more robustly on unseen data by capturing complex, nonlinear anatomical variations. This enables more comprehensive clinical and statistical analyses than the original dataset alone. The resulting synthetic AAA dataset preserves patient privacy while providing a scalable foundation for medical research, device testing, and computational modeling.

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@article{fabbri2025_2506.13628,
  title={ Graph-Convolutional-Beta-VAE for Synthetic Abdominal Aorta Aneurysm Generation },
  author={ Francesco Fabbri and Martino Andrea Scarpolini and Angelo Iollo and Francesco Viola and Francesco Tudisco },
  journal={arXiv preprint arXiv:2506.13628},
  year={ 2025 }
}
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