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

2025-06-16

AI Summary

Addressing the scarcity and privacy sensitivity of real-world abdominal aortic aneurysm (AAA) data—which severely constrain modeling—this paper proposes a graph convolutional network (GCN)-enhanced Beta-VAE framework, the first to integrate GCN with Beta-VAE for disentangled representation learning of non-Euclidean 3D anatomical structures. We innovatively introduce a Procrustes-guided low-perturbation augmentation strategy to enhance generalization while preserving geometric integrity, and design a deterministic–stochastic collaborative generation mechanism to balance morphological fidelity and diversity. Under few-shot conditions, the synthesized data exhibit anatomical plausibility and statistical robustness; generalization error on unseen morphologies is reduced by 37% compared to PCA-based methods. The framework enables privacy-preserving clinical analysis, medical device simulation, and computational modeling.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

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.

Problem

• Generates synthetic AAA data to address privacy and scale issues
• Captures complex anatomical features with a beta-VAE-GCN framework
• Enhances data diversity and realism for clinical analysis

Innovation

• Beta-VAE with Graph Convolution for AAA generation
• Procrustes-based augmentation preserves anatomical integrity
• Deterministic and stochastic strategies enhance data diversity