Aneumo: A Large-Scale Multimodal Aneurysm Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks

2025-05-19

AI Summary

Assessing intracranial aneurysm (IA) rupture risk remains challenging due to incomplete understanding of underlying hemodynamic mechanisms and the high computational cost of conventional CFD simulations, hindering clinical deployment. To address this, we introduce the first large-scale, multimodal IA dataset comprising 10,660 synthetic 3D morphologies, 85,280 high-fidelity steady-state CFD flow fields, and corresponding segmentation masks—enabling for the first time morphology-controllable aneurysm evolution integrated with batch CFD simulation. We propose a benchmark deep learning framework for hemodynamic parameter prediction, unifying multi-source alignment across CT imaging, geometric deformations, segmentation masks, and flow fields. Our open-source release includes both data and code, grounded in baseline morphologies from 427 real-world patient cases. This work bridges biophysical fluid simulation and clinical AI, substantially enhancing model generalizability and clinical interpretability.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Intracranial aneurysms (IAs) are serious cerebrovascular lesions found in approximately 5% of the general population. Their rupture may lead to high mortality. Current methods for assessing IA risk focus on morphological and patient-specific factors, but the hemodynamic influences on IA development and rupture remain unclear. While accurate for hemodynamic studies, conventional computational fluid dynamics (CFD) methods are computationally intensive, hindering their deployment in large-scale or real-time clinical applications. To address this challenge, we curated a large-scale, high-fidelity aneurysm CFD dataset to facilitate the development of efficient machine learning algorithms for such applications. Based on 427 real aneurysm geometries, we synthesized 10,660 3D shapes via controlled deformation to simulate aneurysm evolution. The authenticity of these synthetic shapes was confirmed by neurosurgeons. CFD computations were performed on each shape under eight steady-state mass flow conditions, generating a total of 85,280 blood flow dynamics data covering key parameters. Furthermore, the dataset includes segmentation masks, which can support tasks that use images, point clouds or other multimodal data as input. Additionally, we introduced a benchmark for estimating flow parameters to assess current modeling methods. This dataset aims to advance aneurysm research and promote data-driven approaches in biofluids, biomedical engineering, and clinical risk assessment. The code and dataset are available at: https://github.com/Xigui-Li/Aneumo.

Problem

• Assessing intracranial aneurysm rupture risk with hemodynamic factors
• Overcoming computational limitations of CFD in clinical applications
• Providing multimodal dataset for machine learning in aneurysm research

Innovation

• Large-scale multimodal aneurysm dataset with CFD simulations
• Synthetic 3D shapes simulate aneurysm evolution via deformation
• Deep learning benchmarks for flow parameter estimation