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

TL;DR

This paper introduces a graph-convolutional beta-VAE framework for generating realistic synthetic abdominal aorta aneurysm data, improving data diversity and privacy preservation for medical research.

Contribution

It presents a novel graph-based beta-VAE model that captures complex anatomical variations and employs data augmentation to generate realistic synthetic AAA data.

Findings

Outperforms PCA-based methods on unseen data

Captures complex nonlinear anatomical features

Enhances data diversity and privacy protection

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.