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    • 6. 发明授权
    • Context driven image mining to generate image-based biomarkers
    • 上下文驱动的图像挖掘生成基于图像的生物标志物
    • US08594410B2
    • 2013-11-26
    • US12930873
    • 2011-01-18
    • Guenter SchmidtGerd BinnigRalf SchoenmeyerArno Schaepe
    • Guenter SchmidtGerd BinnigRalf SchoenmeyerArno Schaepe
    • G06K9/00G06K9/62
    • G06T7/0014G01N2800/00G06K9/6253G06T7/0012G06T2207/10116G06T2207/30004G06T2207/30061G06T2207/30068
    • An image-based biomarker is generated using image features obtained through object-oriented image analysis of medical images. The values of a first subset of image features are measured and weighted. The weighted values of the image features are summed to calculate the magnitude of a first image-based biomarker. The magnitude of the biomarker for each patient is correlated with a clinical endpoint, such as a survival time, that was observed for the patient whose medical images were analyzed. The correlation is displayed on a graphical user interface as a scatter plot. A second subset of image features is selected that belong to a second image-based biomarker such that the magnitudes of the second image-based biomarker for the patients better correlate with the clinical endpoints observed for those patients. The second biomarker can then be used to predict the clinical endpoint of other patients whose clinical endpoints have not yet been observed.
    • 使用通过医学图像的面向对象图像分析获得的图像特征来生成基于图像的生物标志物。 测量和加权图像特征的第一子集的值。 将图像特征的加权值相加以计算第一基于图像的生物标志物的大小。 每个患者的生物标志物的大小与对其医学图像分析的患者观察到的临床终点相关,例如存活时间。 相关性作为散点图显示在图形用户界面上。 选择属于第二基于图像的生物标志物的图像特征的第二子集,使得用于患者的第二基于图像的生物标志物的量级与对于那些患者观察到的临床终点更为相关。 然后可以将第二种生物标志物用于预测尚未观察到其临床终点的其他患者的临床终点。
    • 8. 发明申请
    • Context driven image mining to generate image-based biomarkers
    • 上下文驱动的图像挖掘生成基于图像的生物标志物
    • US20110122138A1
    • 2011-05-26
    • US12930873
    • 2011-01-18
    • Guenter SchmidtGerd BinnigRalf SchoenmeyerArno Schaepe
    • Guenter SchmidtGerd BinnigRalf SchoenmeyerArno Schaepe
    • G06T11/20G06K9/00
    • G06T7/0014G01N2800/00G06K9/6253G06T7/0012G06T2207/10116G06T2207/30004G06T2207/30061G06T2207/30068
    • An image-based biomarker is generated using image features obtained through object-oriented image analysis of medical images. The values of a first subset of image features are measured and weighted. The weighted values of the image features are summed to calculate the magnitude of a first image-based biomarker. The magnitude of the biomarker for each patient is correlated with a clinical endpoint, such as a survival time, that was observed for the patient whose medical images were analyzed. The correlation is displayed on a graphical user interface as a scatter plot. A second subset of image features is selected that belong to a second image-based biomarker such that the magnitudes of the second image-based biomarker for the patients better correlate with the clinical endpoints observed for those patients. The second biomarker can then be used to predict the clinical endpoint of other patients whose clinical endpoints have not yet been observed.
    • 使用通过医学图像的面向对象图像分析获得的图像特征来生成基于图像的生物标志物。 测量和加权图像特征的第一子集的值。 将图像特征的加权值相加以计算第一基于图像的生物标志物的大小。 每个患者的生物标志物的大小与对其医学图像分析的患者观察到的临床终点相关,例如存活时间。 相关性作为散点图显示在图形用户界面上。 选择属于第二基于图像的生物标志物的图像特征的第二子集,使得用于患者的第二基于图像的生物标志物的量级与对于那些患者观察到的临床终点更为相关。 然后可以将第二种生物标志物用于预测尚未观察到其临床终点的其他患者的临床终点。