Aneumo: A Large-Scale Comprehensive Synthetic Dataset of Aneurysm Hemodynamics

2025-01-17

AI Summary

To address the scarcity of clinical intracranial aneurysm data and limited resources for hemodynamic model validation, this study proposes a novel aneurysm synthesis paradigm integrating morphological resampling with parametric deformation. We introduce the first large-scale, open-source dataset featuring physically meaningful hemodynamic parameters: it comprises 466 real patient-derived models and 10,000 synthetically generated models—including aneurysm-free controls—each accompanied by medical-image-grade segmentation masks. All models undergo steady-state computational fluid dynamics (CFD) simulations across eight physiological flow conditions, yielding velocity, pressure, and wall shear stress fields. This dataset uniquely bridges the gap in synthetic aneurysm data endowed with realistic anatomical constraints and interpretable hemodynamic features. It substantially advances rupture risk prediction, pathophysiological mechanism investigation, and training of AI-assisted diagnostic models. The dataset is publicly available on GitHub.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Application Domains: Humanities & Computational Social Science

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Intracranial aneurysm (IA) is a common cerebrovascular disease that is usually asymptomatic but may cause severe subarachnoid hemorrhage (SAH) if ruptured. Although clinical practice is usually based on individual factors and morphological features of the aneurysm, its pathophysiology and hemodynamic mechanisms remain controversial. To address the limitations of current research, this study constructed a comprehensive hemodynamic dataset of intracranial aneurysms. The dataset is based on 466 real aneurysm models, and 10,000 synthetic models were generated by resection and deformation operations, including 466 aneurysm-free models and 9,534 deformed aneurysm models. The dataset also provides medical image-like segmentation mask files to support insightful analysis. In addition, the dataset contains hemodynamic data measured at eight steady-state flow rates (0.001 to 0.004 kg/s), including critical parameters such as flow velocity, pressure, and wall shear stress, providing a valuable resource for investigating aneurysm pathogenesis and clinical prediction. This dataset will help advance the understanding of the pathologic features and hemodynamic mechanisms of intracranial aneurysms and support in-depth research in related fields. Dataset hosted at https://github.com/Xigui-Li/Aneumo.

Problem

• Cerebral Aneurysms
• Hemodynamics
• Predictive Modeling

Innovation

• Aneumo Dataset
• Cerebral Aneurysm Hemodynamics
• Artificially Generated Models