Based on CT and MRI images acquired from normal pressure hydrocephalus (NPH) patients, using machine learning methods, we aim to establish a multi-modal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume. First, we extract the brain CT and MRI images of 143 definite NPH patients. Second, we manually label the ventricular volume (VV) and intracranial volume (ICV). Then, we use machine learning method to extract features and establish automatic ventricle segmentation model. Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV. In CT images, the Dice similarity coefficient (DSC), Intraclass Correlation Coefficient (ICC), Pearson correlation, and Bland-Altman analysis of the automatic and manual segmentation result of the VV were 0.95, 0.99, 0.99, and 4.2$\pm$2.6 respectively. The results of ICV were 0.96, 0.99, 0.99, and 6.0$\pm$3.8 respectively. The whole process takes 3.4$\pm$0.3 seconds. In MRI images, the DSC, ICC, Pearson correlation, and Bland-Altman analysis of the automatic and manual segmentation result of the VV were 0.94, 0.99, 0.99, and 2.0$\pm$0.6 respectively. The results of ICV were 0.93, 0.99, 0.99, and 7.9$\pm$3.8 respectively. The whole process took 1.9$\pm$0.1 seconds. We have established a multi-modal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume of NPH patients. This can help clinicians quickly and accurately understand the situation of NPH patient's ventricles.
翻译:根据正常压力脑积水(NPH)患者获得的CT和MRI图像,利用机器学习方法,利用机器学习方法提取功能,建立自动心电图解析模型,并实现VV和ICV的自动测量。首先,我们提取143名非NPH病人的大脑CT和MRI图像。第二,我们人工标注VV的心血管体积和内部体积。然后,我们使用机器学习方法提取功能,建立自动心电图解解析模型。最后,我们核查模型的可靠性,并实现了VVV和ICV的自动自动断裂分解法。 在CT的图像中,Dice相似系数(DSC)、Intracolal Corrolation Coparative(ICC)、Pearson 相关图像和Bland-Altman对VVV的自动和手解析分析结果分别为0.95、0.99 N99、0.99美元和4.2美元内心电解剖结果。ICVV的直径0.69、0.99 0.99 和6.00美元内心电流流流解结果、BS-IS的自动解分析分别采用3.4m。