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    • 5. 发明申请
    • System and Method for the Validation and Quality Assurance of Computerized Contours of Human Anatomy
    • 人体解剖学电脑轮廓的验证和质量保证体系与方法
    • US20150297916A1
    • 2015-10-22
    • US14700592
    • 2015-04-30
    • Washington University in St. Louis
    • Hsin-Chen ChenSasa MuticJun TanMichael AltmanJames KavanaughHua Li
    • A61N5/10
    • A61N5/1039G06T7/13G06T2207/10081G06T2207/10116G06T2207/20081G06T2207/30004
    • A system and method for validating the accuracy of delineated contours in computerized imaging using statistical data for generating assessment criterion that define acceptable tolerances for delineated contours, with the statistical data being conditionally updated and/or refined between individual processes for validating delineated contours to thereby adjust the tolerances defined by the assessment criterion in the stored statistical data, such that the stored statistical data is more closely representative of a target population. In an alternative embodiment, a system and method for validating the accuracy of delineated contours in computerized imaging using machine learning for assessing delineated contours, with the machine learning training data being used to generate geometric attributes, and the geometric attributes used to construct intra- and interstructural geometric attribute distribution models to automatically detect contouring errors. The present invention may be used to facilitate, as one example, radiation therapy.
    • 用于使用用于生成评估标准的统计数据来验证计算机化成像中的描绘轮廓的准确度的系统和方法,所述评估标准定义了描绘轮廓的可接受公差,其中统计数据在用于验证所描绘的轮廓之间的各个过程之间有条件地更新和/或改进以由此调整 在所存储的统计数据中由评估标准定义的公差,使得存储的统计数据更接近于目标群体的代表。 在替代实施例中,一种系统和方法,用于使用机器学习来评估计算机化学习中描绘的轮廓的准确性的系统和方法,其中机器学习训练数据用于生成几何属性,以及用于构建内部和内部 结构几何属性分布模型自动检测轮廓误差。 作为一个示例,本发明可以用于促进放射治疗。