Aneumo: A Large-Scale Comprehensive Synthetic Dataset of Aneurysm Hemodynamics
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866929679928131584 |
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| author | Li, Xigui Zhou, Yuanye Xiao, Feiyang Guo, Xin Zhang, Yichi Jiang, Chen Ge, Jianchao Wang, Xiansheng Wang, Qimeng Zhang, Taiwei Lin, Chensen Cheng, Yuan Qi, Yuan |
| author_facet | Li, Xigui Zhou, Yuanye Xiao, Feiyang Guo, Xin Zhang, Yichi Jiang, Chen Ge, Jianchao Wang, Xiansheng Wang, Qimeng Zhang, Taiwei Lin, Chensen Cheng, Yuan Qi, Yuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_09980 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Aneumo: A Large-Scale Comprehensive Synthetic Dataset of Aneurysm Hemodynamics Li, Xigui Zhou, Yuanye Xiao, Feiyang Guo, Xin Zhang, Yichi Jiang, Chen Ge, Jianchao Wang, Xiansheng Wang, Qimeng Zhang, Taiwei Lin, Chensen Cheng, Yuan Qi, Yuan Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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. |
| title | Aneumo: A Large-Scale Comprehensive Synthetic Dataset of Aneurysm Hemodynamics |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2501.09980 |