Graph-Convolution-Beta-VAE for Synthetic Abdominal Aorta Aneurysm Generation
Journal:
arXiv
Published Date:
Jun 16, 2025
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.
