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    Method and system for polyp segmentation for 3D computed tomography colonography
    4.
    发明申请
    Method and system for polyp segmentation for 3D computed tomography colonography 有权
    用于3D计算机断层扫描结构的息肉分割方法和系统

    公开(公告)号：US20090074272A1

    公开(公告)日：2009-03-19

    申请号：US12231772

    申请日：2008-09-05

    申请人： Le Lu ,  Adrian Barbu ,  Matthias Wolf ,  Sarang Lakare ,  Luca Bogoni ,  Marcos Salganicoff ,  Dorin Comaniciu

    发明人： Le Lu ,  Adrian Barbu ,  Matthias Wolf ,  Sarang Lakare ,  Luca Bogoni ,  Marcos Salganicoff ,  Dorin Comaniciu

    IPC分类号： G06K9/00

    CPC分类号： G06T7/0012 ,  G06T7/11 ,  G06T7/143 ,  G06T2207/10081 ,  G06T2207/20076 ,  G06T2207/30032

    摘要： A method and system for polyp segmentation in computed tomography colonogrphy (CTC) volumes is disclosed. The polyp segmentation method utilizes a three-staged probabilistic binary classification approach for automatically segmenting polyp voxels from surrounding tissue in CTC volumes. Based on an input initial polyp position, a polyp tip is detected in a CTC volume using a trained 3D point detector. A local polar coordinate system is then fit to the colon surface in the CTC volume with the origin at the detected polyp tip. Polyp interior voxels and polyp exterior voxels are detected along each axis of the local polar coordinate system using a trained 3D box. A boundary voxel is detected on each axis of the local polar coordinate system based on the detected polyp interior voxels and polyp exterior voxels by boosted 1D curve parsing using a trained classifier. This results in a segmented polyp boundary.

    摘要翻译： 公开了一种计算机断层扫描（CTC）体积中息肉分割的方法和系统。 息肉分割方法采用三阶段概率二分类方法，自动分割CTC体积周围组织的息肉体素。 基于输入的初始息肉位置，使用训练有素的3D点检测器在CTC体积中检测息肉末端。 然后将局部极坐标系拟合到CTC体积中的结肠表面，其中原点在检测到的息肉末端。 使用训练有素的3D框，在局部极坐标系的每个轴上检测Polyp内部体素和息肉外部体素。 基于检测到的息肉内部体素和息肉外部体素，通过使用训练有素的分类器进行升压1D曲线解析，在局部极坐标系的每个轴上检测边界体素。 这导致分段息肉边界。

    User interface for polyp annotation, segmentation, and measurement in 3D computed tomography colonography
    5.
    发明申请
    User interface for polyp annotation, segmentation, and measurement in 3D computed tomography colonography 有权
    用于三维计算机断层扫描结构中息肉注解，分割和测量的用户界面

    公开(公告)号：US20090080747A1

    公开(公告)日：2009-03-26

    申请号：US12231771

    申请日：2008-09-05

    申请人： Le Lu ,  Adrian Barbu ,  Matthias Wolf ,  Sarang Lakare ,  Luca Bogoni ,  Marcos Salganicoff ,  Dorin Comaniciu

    发明人： Le Lu ,  Adrian Barbu ,  Matthias Wolf ,  Sarang Lakare ,  Luca Bogoni ,  Marcos Salganicoff ,  Dorin Comaniciu

    IPC分类号： G06K9/00

    CPC分类号： G16H40/63 ,  G06F19/00 ,  G06T7/0012 ,  G06T7/12 ,  G06T2200/24 ,  G06T2207/10072 ,  G06T2207/20092 ,  G06T2207/30032

    摘要： A method and system for providing a user interface for polyp annotation, segmentation, and measurement in computer tomography colonography (CTC) volumes is disclosed. The interface receives an initial polyp position in a CTC volume, and automatically segments the polyp based on the initial polyp position. In order to segment the polyp, a polyp tip is detected in the CTC volume using a trained 3D point detector. A local polar coordinate system is then fit to the colon surface in the CTC volume with the origin at the detected polyp tip. Polyp interior voxels and polyp exterior voxels are detected along each axis of the local polar coordinate system using a trained 3D box. A boundary voxel is detected on each axis of the local polar coordinate system based on the detected polyp interior voxels and polyp exterior voxels by boosted 1D curve parsing using a trained classifier. This results in a segmented polyp boundary. The segmented polyp is displayed in the user interface, and a user can modify the segmented polyp boundary using the interface. The interface can measure the size of the segmented polyp in three dimensions. The user can also use the interface for polyp annotation in CTC volumes.

    摘要翻译： 公开了一种用于在计算机断层造影（CTC）体积中提供用于息肉注释，分割和测量的用户界面的方法和系统。 界面在CTC体积中接收初始息肉位置，并根据初始息肉位置自动分段息肉。 为了分割息肉，使用训练有素的3D点检测器在CTC体积中检测息肉末端。 然后将局部极坐标系拟合到CTC体积中的结肠表面，其中原点在检测到的息肉末端。 使用训练有素的3D框，在局部极坐标系的每个轴上检测Polyp内部体素和息肉外部体素。 基于检测到的息肉内部体素和息肉外部体素，通过使用训练有素的分类器进行升压1D曲线解析，在局部极坐标系的每个轴上检测边界体素。 这导致分段息肉边界。 分段息肉显示在用户界面中，用户可以使用界面修改分段息肉边界。 界面可以在三维中测量分段息肉的大小。 用户还可以在CTC卷中使用界面进行息肉注释。

    Matching of regions of interest across multiple views
    6.
    发明授权
    Matching of regions of interest across multiple views 有权
    在多个视图中匹配感兴趣的区域

    公开(公告)号：US08885898B2

    公开(公告)日：2014-11-11

    申请号：US13267095

    申请日：2011-10-06

    申请人： Meizhu Liu ,  Le Lu ,  Vikas C. Raykar ,  Marcos Salganicoff ,  Matthias Wolf

    发明人： Meizhu Liu ,  Le Lu ,  Vikas C. Raykar ,  Marcos Salganicoff ,  Matthias Wolf

    IPC分类号： G06K9/00 ,  G06T7/00 ,  G06K9/62 ,  A61B6/03 ,  A61B8/13

    CPC分类号： G06K9/6215 ,  A61B6/032 ,  A61B6/037 ,  A61B8/13 ,  G06T7/0014 ,  G06T7/30 ,  G06T2207/10072 ,  G06T2207/20081 ,  G06T2207/30032

    摘要： Described herein is a framework for multi-view matching of regions of interest in images. According to one aspect, a processor receives first and second digitized images, as well as at least one CAD finding corresponding to a detected region of interest in the first image. The processor determines at least one candidate location in the second image that matches the CAD finding in the first image. The matching is performed based on local appearance features extracted for the CAD finding and the candidate location. In accordance with another aspect, the processor receives digitized training images representative of at least first and second views of one or more regions of interest. Feature selection is performed based on the training images to select a subset of relevant local appearance features to represent instances in the first and second views. A distance metric is then learned based on the subset of local appearance features. The distance metric may be used to perform matching of the regions of interest.

    摘要翻译： 这里描述了用于图像中感兴趣区域的多视图匹配的框架。 根据一个方面，处理器接收第一和第二数字化图像，以及对应于第一图像中检测到的感兴趣区域的至少一个CAD查找。 处理器确定与第一图像中的CAD查找匹配的第二图像中的至少一个候选位置。 基于为CAD查找和候选位置提取的本地外观特征执行匹配。 根据另一方面，处理器接收代表一个或多个感兴趣区域的至少第一和第二视图的数字化的训练图像。 基于训练图像执行特征选择，以选择相关局部外观特征的子集来表示第一和第二视图中的实例。 然后基于局部外观特征的子集来学习距离度量。 距离度量可以用于执行感兴趣区域的匹配。

    Matching of Regions of Interest Across Multiple Views
    7.
    发明申请
    Matching of Regions of Interest Across Multiple Views 有权
    多个意见区域的匹配

    公开(公告)号：US20120088981A1

    公开(公告)日：2012-04-12

    申请号：US13267095

    申请日：2011-10-06

    申请人： Meizhu Liu ,  Le Lu ,  Vikas C. Raykar ,  Marcos Salganicoff ,  Matthias Wolf

    发明人： Meizhu Liu ,  Le Lu ,  Vikas C. Raykar ,  Marcos Salganicoff ,  Matthias Wolf

    IPC分类号： A61B5/00 ,  G06K9/00

    CPC分类号： G06K9/6215 ,  A61B6/032 ,  A61B6/037 ,  A61B8/13 ,  G06T7/0014 ,  G06T7/30 ,  G06T2207/10072 ,  G06T2207/20081 ,  G06T2207/30032

    摘要： Described herein is a framework for multi-view matching of regions of interest in images. According to one aspect, a processor receives first and second digitized images, as well as at least one CAD finding corresponding to a detected region of interest in the first image. The processor determines at least one candidate location in the second image that matches the CAD finding in the first image. The matching is performed based on local appearance features extracted for the CAD finding and the candidate location. In accordance with another aspect, the processor receives digitized training images representative of at least first and second views of one or more regions of interest. Feature selection is performed based on the training images to select a subset of relevant local appearance features to represent instances in the first and second views. A distance metric is then learned based on the subset of local appearance features. The distance metric may be used to perform matching of the regions of interest.

    摘要翻译： 这里描述了用于图像中感兴趣区域的多视图匹配的框架。 根据一个方面，处理器接收第一和第二数字化图像，以及对应于第一图像中检测到的感兴趣区域的至少一个CAD查找。 处理器确定与第一图像中的CAD查找匹配的第二图像中的至少一个候选位置。 基于为CAD查找和候选位置提取的本地外观特征执行匹配。 根据另一方面，处理器接收代表一个或多个感兴趣区域的至少第一和第二视图的数字化的训练图像。 基于训练图像执行特征选择，以选择相关局部外观特征的子集来表示第一和第二视图中的实例。 然后基于局部外观特征的子集来学习距离度量。 距离度量可以用于执行感兴趣区域的匹配。

    Multi-level contextual learning of data
    8.
    发明授权
    Multi-level contextual learning of data 有权
    数据的多层次上下文学习

    公开(公告)号：US08724866B2

    公开(公告)日：2014-05-13

    申请号：US12962901

    申请日：2010-12-08

    申请人： Dijia Wu ,  Le Lu ,  Jinbo Bi ,  Yoshihisa Shinagawa ,  Marcos Salganicoff

    发明人： Dijia Wu ,  Le Lu ,  Jinbo Bi ,  Yoshihisa Shinagawa ,  Marcos Salganicoff

    IPC分类号： G06K9/00

    CPC分类号： G06K9/4638 ,  G06K2209/05

    摘要： Described herein is a framework for automatically classifying a structure in digital image data are described herein. In one implementation, a first set of features is extracted from digital image data, and used to learn a discriminative model. The discriminative model may be associated with at least one conditional probability of a class label given an image data observation Based on the conditional probability, at least one likelihood measure of the structure co-occurring with another structure in the same sub-volume of the digital image data is determined. A second set of features may then be extracted from the likelihood measure.

    摘要翻译： 这里描述了用于自动分类数字图像数据中的结构的框架。 在一个实现中，从数字图像数据中提取第一组特征，并用于学习辨别模型。 鉴别模型可以与给定图像数据观察的类标签的至少一个条件概率相关联。基于条件概率，与数字的相同子体积中的另一结构共同出现的结构的至少一个似然度量 确定图像数据。 然后可以从似然度量中提取第二组特征。

    Multi-Level Contextual Learning of Data
    9.
    发明申请
    Multi-Level Contextual Learning of Data 有权
    数据的多层次上下文学习

    公开(公告)号：US20110075920A1

    公开(公告)日：2011-03-31

    申请号：US12962901

    申请日：2010-12-08

    申请人： Dijia Wu ,  Le Lu ,  Jinbo Bi ,  Yoshihisa Shinagawa ,  Marcos Salganicoff

    发明人： Dijia Wu ,  Le Lu ,  Jinbo Bi ,  Yoshihisa Shinagawa ,  Marcos Salganicoff

    IPC分类号： G06K9/62

    CPC分类号： G06K9/4638 ,  G06K2209/05

    摘要： Described herein is a framework for automatically classifying a structure in digital image data are described herein. In one implementation, a first set of features is extracted from digital image data, and used to learn a discriminative model. The discriminative model may be associated with at least one conditional probability of a class label given an image data observation Based on the conditional probability, at least one likelihood measure of the structure co-occurring with another structure in the same sub-volume of the digital image data is determined. A second set of features may then be extracted from the likelihood measure.

    摘要翻译： 这里描述了用于自动分类数字图像数据中的结构的框架。 在一个实现中，从数字图像数据中提取第一组特征，并用于学习辨别模型。 鉴别模型可以与给定图像数据观察的类标签的至少一个条件概率相关联。基于条件概率，与数字的相同子体积中的另一结构共同出现的结构的至少一个似然度量 确定图像数据。 然后可以从似然度量中提取第二组特征。

    Method and system for polyp segmentation for 3D computed tomography colonography
    10.
    发明授权
    Method and system for polyp segmentation for 3D computed tomography colonography 有权
    用于3D计算机断层扫描结构的息肉分割方法和系统

    公开(公告)号：US08184888B2

    公开(公告)日：2012-05-22

    申请号：US12231772

    申请日：2008-09-05

    申请人： Le Lu ,  Adrian Barbu ,  Matthias Wolf ,  Sarang Lakare ,  Luca Bogoni ,  Marcos Salganicoff ,  Dorin Comaniciu

    发明人： Le Lu ,  Adrian Barbu ,  Matthias Wolf ,  Sarang Lakare ,  Luca Bogoni ,  Marcos Salganicoff ,  Dorin Comaniciu

    IPC分类号： G06K9/46

    CPC分类号： G06T7/0012 ,  G06T7/11 ,  G06T7/143 ,  G06T2207/10081 ,  G06T2207/20076 ,  G06T2207/30032

    摘要： A method and system for polyp segmentation in computed tomography colonogrphy (CTC) volumes is disclosed. The polyp segmentation method utilizes a three-staged probabilistic binary classification approach for automatically segmenting polyp voxels from surrounding tissue in CTC volumes. Based on an input initial polyp position, a polyp tip is detected in a CTC volume using a trained 3D point detector. A local polar coordinate system is then fit to the colon surface in the CTC volume with the origin at the detected polyp tip. Polyp interior voxels and polyp exterior voxels are detected along each axis of the local polar coordinate system using a trained 3D box. A boundary voxel is detected on each axis of the local polar coordinate system based on the detected polyp interior voxels and polyp exterior voxels by boosted 1D curve parsing using a trained classifier. This results in a segmented polyp boundary.

    摘要翻译： 公开了一种计算机断层扫描（CTC）体积中息肉分割的方法和系统。 息肉分割方法采用三阶段概率二分类方法，自动分割CTC体积周围组织的息肉体素。 基于输入的初始息肉位置，使用训练有素的3D点检测器在CTC体积中检测息肉末端。 然后将局部极坐标系拟合到CTC体积中的结肠表面，其中原点在检测到的息肉末端。 使用训练有素的3D框，在局部极坐标系的每个轴上检测Polyp内部体素和息肉外部体素。 基于检测到的息肉内部体素和息肉外部体素，通过使用训练有素的分类器进行升压1D曲线解析，在局部极坐标系的每个轴上检测边界体素。 这导致分段息肉边界。