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    Method and system for up-vector detection for ribs in computed tomography volumes
    1.
    发明授权
    Method and system for up-vector detection for ribs in computed tomography volumes 有权
    计算机断层扫描体积中肋骨向上检测的方法和系统

    公开(公告)号：US09020233B2

    公开(公告)日：2015-04-28

    申请号：US13602660

    申请日：2012-09-04

    申请人： David Liu ,  Hao Xu ,  Dijia Wu ,  Christian Tietjen ,  Grzegorz Soza ,  Shaohua Kevin Zhou ,  Dorin Comaniciu

    发明人： David Liu ,  Hao Xu ,  Dijia Wu ,  Christian Tietjen ,  Grzegorz Soza ,  Shaohua Kevin Zhou ,  Dorin Comaniciu

    IPC分类号： G06K9/00 ,  G06K9/48 ,  G06K9/62

    CPC分类号： G06K9/00 ,  G06K9/48 ,  G06K9/62

    摘要： A method and system for up-vector detection for ribs in a 3D medical image volume, such as a computed tomography (CT) volume is disclosed. A rib centerline of at least one rib is extracted in a 3D medical image volume. An up-vector is automatically detected at each of a plurality of centerline points of the rib centerline of the at least one rib. The up-vector at each centerline point can be detected using a trained regression function. Alternatively, the up-vector at each centerline point can be detected by detecting an ellipse shape in a cross-sectional rib image generated at each centerline point.

    摘要翻译： 公开了一种用于3D医学图像体积中的肋的上矢量检测的方法和系统，例如计算机断层摄影（CT）体积。 在3D医学图像体积中提取至少一个肋的肋中心线。 在至少一个肋的肋中心线的多个中心线点的每一个处自动检测向上矢量。 可以使用经过训练的回归函数检测每个中心线点处的向上矢量。 或者，可以通过在每个中心线点处产生的横截面肋图像中检测椭圆形状来检测每个中心线点处的向上矢量。

    Method and System for Up-Vector Detection for Ribs in Computed Tomography Volumes
    2.
    发明申请
    Method and System for Up-Vector Detection for Ribs in Computed Tomography Volumes 有权
    计算机断层扫描中肋骨上向量检测的方法和系统

    公开(公告)号：US20130070996A1

    公开(公告)日：2013-03-21

    申请号：US13602660

    申请日：2012-09-04

    申请人： David Liu ,  Hao Xu ,  Dijia Wu ,  Christian Tietjen ,  Grzegorz Soza ,  Shaohua Kevin Zhou ,  Dorin Comaniciu

    发明人： David Liu ,  Hao Xu ,  Dijia Wu ,  Christian Tietjen ,  Grzegorz Soza ,  Shaohua Kevin Zhou ,  Dorin Comaniciu

    IPC分类号： G06K9/48 ,  G06K9/62 ,  G06K9/00

    CPC分类号： G06K9/00 ,  G06K9/48 ,  G06K9/62

    摘要： A method and system for up-vector detection for ribs in a 3D medical image volume, such as a computed tomography (CT) volume is disclosed. A rib centerline of at least one rib is extracted in a 3D medical image volume. An up-vector is automatically detected at each of a plurality of centerline points of the rib centerline of the at least one rib. The up-vector at each centerline point can be detected using a trained regression function. Alternatively, the up-vector at each centerline point can be detected by detecting an ellipse shape in a cross-sectional rib image generated at each centerline point.

    摘要翻译： 公开了一种用于3D医学图像体积中的肋的上矢量检测的方法和系统，例如计算机断层摄影（CT）体积。 在3D医学图像体积中提取至少一个肋的肋中心线。 在至少一个肋的肋中心线的多个中心线点的每一个处自动检测向上矢量。 可以使用经过训练的回归函数检测每个中心线点处的向上矢量。 或者，可以通过在每个中心线点处产生的横截面肋图像中检测椭圆形状来检测每个中心线点处的向上矢量。

    Method and system for training a landmark detector using multiple instance learning
    7.
    发明授权
    Method and system for training a landmark detector using multiple instance learning 有权
    使用多实例学习训练地标探测器的方法和系统

    公开(公告)号：US08588519B2

    公开(公告)日：2013-11-19

    申请号：US13228509

    申请日：2011-09-09

    申请人： David Liu ,  Shaohua Kevin Zhou ,  Paul Swoboda ,  Dorin Comaniciu ,  Christian Tietjen

    发明人： David Liu ,  Shaohua Kevin Zhou ,  Paul Swoboda ,  Dorin Comaniciu ,  Christian Tietjen

    IPC分类号： G06K9/52

    CPC分类号： G06K9/6257 ,  G06K2209/051

    摘要： An apparatus and method for training a landmark detector receives training data which includes a plurality of positive training bags, each including a plurality of positively annotated instances, and a plurality of negative training bags, each including at least one negatively annotated instance. Classification function is initialized by training a first weak classifier based on the positive training bags and the negative training bags. All training instances are evaluated using the classification function. For each of a plurality of remaining classifiers, a cost value gradient is calculated based on spatial context information of each instance in each positive bag evaluated by the classification function. A gradient value associated with each of the remaining weak classifiers is calculated based on the cost value gradients, and a weak classifier is selected which has a lowest associated gradient value and given a weighting parameter and added to the classification function.

    摘要翻译： 用于训练地标检测器的装置和方法接收训练数据，训练数据包括多个正训练袋，每个正训练袋包括多个带有正面注释的实例，以及多个负训练袋，每个包括至少一个负注释实例。 基于积极的训练袋和负面训练袋训练第一个弱分类器来初始化分类功能。 使用分类函数评估所有训练实例。 对于多个剩余分类器中的每一个，基于由分类函数评估的每个正包中的每个实例的空间上下文信息来计算成本值梯度。 基于成本值梯度计算与剩余弱分类器中的每一个相关联的梯度值，并且选择具有最低相关梯度值并给出加权参数并加到分类函数的弱分类器。