Rethinking Intracranial Aneurysm Vessel Segmentation: A Perspective from Computational Fluid Dynamics Applications

2025-12-01

AI Summary

Existing intracranial aneurysm and parent vessel (IA-Vessel) segmentation methods rely primarily on image-based metrics, neglecting their practical suitability for computational fluid dynamics (CFD) simulations. Method: We introduce the first CFD-oriented, multi-center IAVS dataset (641 cases of 3D MRA) and propose a standardized evaluation framework that prioritizes both topological integrity and CFD usability—using hemodynamic simulation outcomes as the primary validation criterion. We further design a two-stage deep learning segmentation framework enabling end-to-end assessment from image segmentation to automated CFD mesh generation. Contribution/Results: The publicly released dataset, baseline models, source code, and conversion tools significantly enhance the clinical applicability of segmentation methods in hemodynamic analysis. This work establishes a reproducible, verifiable benchmark for AI-driven precision diagnosis and treatment of cerebrovascular diseases.

Technology Category

• Computer Vision: Segmentation
• Humans and AI: Brain-Sensing and Analysis
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

The precise segmentation of intracranial aneurysms and their parent vessels (IA-Vessel) is a critical step for hemodynamic analyses, which mainly depends on computational fluid dynamics (CFD). However, current segmentation methods predominantly focus on image-based evaluation metrics, often neglecting their practical effectiveness in subsequent CFD applications. To address this deficiency, we present the Intracranial Aneurysm Vessel Segmentation (IAVS) dataset, the first comprehensive, multi-center collection comprising 641 3D MRA images with 587 annotations of aneurysms and IA-Vessels. In addition to image-mask pairs, IAVS dataset includes detailed hemodynamic analysis outcomes, addressing the limitations of existing datasets that neglect topological integrity and CFD applicability. To facilitate the development and evaluation of clinically relevant techniques, we construct two evaluation benchmarks including global localization of aneurysms (Stage I) and fine-grained segmentation of IA-Vessel (Stage II) and develop a simple and effective two-stage framework, which can be used as a out-of-the-box method and strong baseline. For comprehensive evaluation of applicability of segmentation results, we establish a standardized CFD applicability evaluation system that enables the automated and consistent conversion of segmentation masks into CFD models, offering an applicability-focused assessment of segmentation outcomes. The dataset, code, and model will be public available at https://github.com/AbsoluteResonance/IAVS.

Problem

• Improves segmentation for CFD hemodynamic analysis
• Addresses lack of topological integrity in existing datasets
• Establishes standardized CFD applicability evaluation system

Innovation

• Introduces first comprehensive multi-center IAVS dataset with hemodynamic data
• Develops two-stage framework for aneurysm localization and vessel segmentation
• Establishes standardized CFD applicability evaluation system for segmentation