构建了大规模颅内动脉瘤血流数据集，助力研究破裂机制与临床预测。
Aneumo: A Large-Scale Comprehensive Synthetic Dataset of Aneurysm Hemodynamics
- 基于466个真实模型生成1万例合成模型，包含变形与无瘤样本
- 涵盖8种稳态流速下的速度、压力、壁面剪切应力等关键参数
- 提供医学图像级分割掩码，适合血管病研究与算法训练
颅内动脉瘤（IA）是一种常见脑血管疾病，常无症状，破裂后可导致严重蛛网膜下腔出血（SAH）。尽管临床实践多依赖个体因素和瘤体形态特征，其病理生理及血流机制仍存争议。为突破现有研究局限，本研究构建了一个全面的颅内动脉瘤血流动力学数据集。该数据集基于466个真实动脉瘤模型，通过截断与形变操作生成10,000个合成模型，包括466个无瘤模型和9,534个变形动脉瘤模型。数据集还提供医学图像风格的分割掩码文件，支持深入分析。同时包含8种稳态流速（0.001至0.004 kg/s）下的血流数据，涵盖流速、压力、壁面剪切应力等关键参数，为动脉瘤发病机制研究与临床预测提供宝贵资源。数据集已托管于https://github.com/Xigui-Li/Aneumo。
原文摘要 · Abstract (English)
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.
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