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Abstract：
BACKGROUND: The rupture of intracranial aneurysms (IAs) would result in subarachnoid hemorrhage with high mortality and disability. Predicting the risk of IAs rupture remains a challenge.
METHODS: This paper proposed an effective method for classifying IAs rupture status by integrating a PointNet-based model and machine learning algorithms. First, medical image segmentation and reconstruction algorithms were applied to 3D Digital Subtraction Angiography (DSA) imaging data to construct three-dimensional IAs geometric models. Geometrical parameters of IAs were then acquired using Geomagic, followed by the computation of hemodynamic clouds and hemodynamic parameters using Computational Fluid Dynamics (CFD). A PointNet-based model was developed to extract different dimensional hemodynamic cloud features. Finally, five types of machine learning algorithms were applied on geometrical parameters, hemodynamic parameters, and hemodynamic cloud features to classify and recognize IAs rupture status. The classification performance of different dimensional hemodynamic cloud features was also compared.
RESULTS: The 16-, 32-, 64-, and 1024-dimensional hemodynamic cloud features were extracted with the PointNet-based model, respectively, and the four types of cloud features in combination with the geometrical parameters and hemodynamic parameters were respectively applied to classify the rupture status of IAs. The best classification outcomes were achieved in the case of 16-dimensional hemodynamic cloud features, the accuracy of XGBoost, CatBoost, SVM, LightGBM, and LR algorithms was 0.887, 0.857, 0.854, 0.857, and 0.908, respectively, and the AUCs were 0.917, 0.934, 0.946, 0.920, and 0.944. In contrast, when only utilizing geometrical parameters and hemodynamic parameters, the accuracies were 0.836, 0.816, 0.826, 0.832, and 0.885, respectively, with AUC values of 0.908, 0.922, 0.930, 0.884, and 0.921.
CONCLUSIONS: In this paper, classification models for IAs rupture status were constructed by integrating a PointNet-based model and machine learning algorithms. Experiments demonstrated that hemodynamic cloud features had a certain contribution weight to the classification of IAs rupture status. When 16-dimensional hemodynamic cloud features were added to the morphological and hemodynamic features, the models achieved the highest classification accuracies and AUCs. Our models and algorithms would provide valuable insights for the clinical diagnosis and treatment of IAs.
METHODS: This paper proposed an effective method for classifying IAs rupture status by integrating a PointNet-based model and machine learning algorithms. First, medical image segmentation and reconstruction algorithms were applied to 3D Digital Subtraction Angiography (DSA) imaging data to construct three-dimensional IAs geometric models. Geometrical parameters of IAs were then acquired using Geomagic, followed by the computation of hemodynamic clouds and hemodynamic parameters using Computational Fluid Dynamics (CFD). A PointNet-based model was developed to extract different dimensional hemodynamic cloud features. Finally, five types of machine learning algorithms were applied on geometrical parameters, hemodynamic parameters, and hemodynamic cloud features to classify and recognize IAs rupture status. The classification performance of different dimensional hemodynamic cloud features was also compared.
RESULTS: The 16-, 32-, 64-, and 1024-dimensional hemodynamic cloud features were extracted with the PointNet-based model, respectively, and the four types of cloud features in combination with the geometrical parameters and hemodynamic parameters were respectively applied to classify the rupture status of IAs. The best classification outcomes were achieved in the case of 16-dimensional hemodynamic cloud features, the accuracy of XGBoost, CatBoost, SVM, LightGBM, and LR algorithms was 0.887, 0.857, 0.854, 0.857, and 0.908, respectively, and the AUCs were 0.917, 0.934, 0.946, 0.920, and 0.944. In contrast, when only utilizing geometrical parameters and hemodynamic parameters, the accuracies were 0.836, 0.816, 0.826, 0.832, and 0.885, respectively, with AUC values of 0.908, 0.922, 0.930, 0.884, and 0.921.
CONCLUSIONS: In this paper, classification models for IAs rupture status were constructed by integrating a PointNet-based model and machine learning algorithms. Experiments demonstrated that hemodynamic cloud features had a certain contribution weight to the classification of IAs rupture status. When 16-dimensional hemodynamic cloud features were added to the morphological and hemodynamic features, the models achieved the highest classification accuracies and AUCs. Our models and algorithms would provide valuable insights for the clinical diagnosis and treatment of IAs.
摘要：
背景：颅内动脉瘤破裂会导致蛛网膜下腔出血，死亡率和致残率高。预测IAs破裂的风险仍然是一个挑战。
方法：本文通过集成基于PointNet的模型和机器学习算法，提出了一种对IAs破裂状态进行分类的有效方法。首先,将医学图像分割和重建算法应用于3D数字减影血管造影（DSA）成像数据，以构建三维IAs几何模型。然后使用Geomagic获取IAs的几何参数，然后使用计算流体动力学（CFD）计算血液动力学云和血液动力学参数。开发了基于PointNet的模型来提取不同维度的血液动力学云特征。最后,五种类型的机器学习算法应用于几何参数，血液动力学参数,和血液动力学云特征来分类和识别IAs破裂状态。还比较了不同维度血流动力学云特征的分类性能。
结果：16-，32-，64-,利用基于PointNet的模型提取1024维血液动力学云特征，分别,并将四种类型的云特征结合几何参数和血液动力学参数分别用于分类IAs的破裂状态。在16维血流动力学云特征的情况下，取得了最佳的分类结果，XGBoost的准确性，CatBoost,SVM,LightGBM,和LR算法分别为0.887、0.857、0.854、0.857和0.908，AUC分别为0.917、0.934、0.946、0.920和0.944。相比之下,当仅利用几何参数和血液动力学参数时，准确度分别为0.836、0.816、0.826、0.832和0.885，AUC值为0.908、0.922、0.930、0.884和0.921。
结论：在本文中，通过集成基于PointNet的模型和机器学习算法，构建了IAs破裂状态的分类模型。实验表明，血液动力学云特征对IAs破裂状态的分类具有一定的贡献权重。当将16维血液动力学云特征添加到形态和血液动力学特征中时，这些模型实现了最高的分类精度和AUC。我们的模型和算法将为IAs的临床诊断和治疗提供有价值的见解。
方法：本文通过集成基于PointNet的模型和机器学习算法，提出了一种对IAs破裂状态进行分类的有效方法。首先,将医学图像分割和重建算法应用于3D数字减影血管造影（DSA）成像数据，以构建三维IAs几何模型。然后使用Geomagic获取IAs的几何参数，然后使用计算流体动力学（CFD）计算血液动力学云和血液动力学参数。开发了基于PointNet的模型来提取不同维度的血液动力学云特征。最后,五种类型的机器学习算法应用于几何参数，血液动力学参数,和血液动力学云特征来分类和识别IAs破裂状态。还比较了不同维度血流动力学云特征的分类性能。
结果：16-，32-，64-,利用基于PointNet的模型提取1024维血液动力学云特征，分别,并将四种类型的云特征结合几何参数和血液动力学参数分别用于分类IAs的破裂状态。在16维血流动力学云特征的情况下，取得了最佳的分类结果，XGBoost的准确性，CatBoost,SVM,LightGBM,和LR算法分别为0.887、0.857、0.854、0.857和0.908，AUC分别为0.917、0.934、0.946、0.920和0.944。相比之下,当仅利用几何参数和血液动力学参数时，准确度分别为0.836、0.816、0.826、0.832和0.885，AUC值为0.908、0.922、0.930、0.884和0.921。
结论：在本文中，通过集成基于PointNet的模型和机器学习算法，构建了IAs破裂状态的分类模型。实验表明，血液动力学云特征对IAs破裂状态的分类具有一定的贡献权重。当将16维血液动力学云特征添加到形态和血液动力学特征中时，这些模型实现了最高的分类精度和AUC。我们的模型和算法将为IAs的临床诊断和治疗提供有价值的见解。
