Building a Synthetic Vascular Model: Evaluation in an Intracranial Aneurysms Detection Scenario

TL;DR

This paper introduces a comprehensive synthetic 3D vascular model mimicking cerebral arteries and aneurysms, aimed at enhancing deep learning detection of intracranial aneurysms through data augmentation.

Contribution

The work presents a novel synthetic vascular model that accurately replicates brain vasculature and aneurysm features for improved deep learning-based detection.

Findings

Synthetic data improves aneurysm detection accuracy

Model accurately reproduces vascular geometry and noise

Enhanced neural network performance with data augmentation

Abstract

We hereby present a full synthetic model, able to mimic the various constituents of the cerebral vascular tree, including the cerebral arteries, bifurcations and intracranial aneurysms. This model intends to provide a substantial dataset of brain arteries which could be used by a 3D convolutional neural network to efficiently detect Intra-Cranial Aneurysms. The cerebral aneurysms most often occur on a particular structure of the vascular tree named the Circle of Willis. Various studies have been conducted to detect and monitor the aneurysms and those based on Deep Learning achieve the best performance. Specifically, in this work, we propose a full synthetic 3D model able to mimic the brain vasculature as acquired by Magnetic Resonance Angiography, Time Of Flight principle. Among the various MRI modalities, this latter allows for a good rendering of the blood vessels and is non-invasive. Our model has been designed to simultaneously mimic the arteries' geometry, the aneurysm shape, and the background noise. The vascular tree geometry is modeled thanks to an interpolation with 3D Spline functions, and the statistical properties of the background noise is collected from angiography acquisitions and reproduced within the model. In this work, we thoroughly describe the synthetic vasculature model, we build up a neural network designed for aneurysm segmentation and detection, finally, we carry out an in-depth evaluation of the performance gap gained thanks to the synthetic model data augmentation.