公开(公告)号：US07558413B2

公开(公告)日：2009-07-07

申请号：US11244798

申请日：2005-10-06

申请人： Zhuowen Tu ,  Xiang Zhou ,  Adrian Barbu ,  Luca Bogoni ,  Dorin Comaniciu

发明人： Zhuowen Tu ,  Xiang Zhou ,  Adrian Barbu ,  Luca Bogoni ,  Dorin Comaniciu

IPC分类号： G06K9/00

CPC分类号： G06K9/4614 ,  G06K9/6206 ,  G06K9/6214 ,  G06K9/6257 ,  G06K2209/053 ,  G06T7/0012 ,  G06T2207/10072 ,  G06T2207/30032

摘要： A method for detecting target objects in a three dimensional (3D) image volume of an anatomical structure is disclosed. A set of candidate locations in the image volume are obtained. For each candidate location, sub-volumes of at least two different scales are cropped out. Each sub-volume comprises a plurality of voxels. For each of the sub-volumes, each sub-volume is rotated in at least two different orientations. A shape classifier is applied to each sub-volume. If the voxels in the sub-volume pass the shape classifier, a gradient direction is computed for the voxels. If the gradient direction for the voxels is one of a predefined orientation, a probability classifier is applied to the voxels. A probability measure computed by the probability classifier as a confidence measure is used for the sub-volume. If the confidence measure is above a predetermined threshold value, the sub-volume is determined to contain the target object.

摘要翻译： 公开了一种用于检测解剖结构的三维（3D）图像体积中的目标对象的方法。 获得图像体积中的一组候选位置。 对于每个候选位置，剪切出至少两个不同尺度的子体积。 每个子体积包括多个体素。 对于每个子卷，每个子卷以至少两个不同的方向旋转。 形状分类器应用于每个子卷。 如果子体积中的体素通过形状分类器，则为体素计算梯度方向。 如果体素的梯度方向是预定义方向之一，则将概率分类器应用于体元。 将概率分类器计算出的概率度量作为置信度量度用于子卷。 如果置信度高于预定阈值，则确定子卷包含目标对象。

公开(公告)号：US07583831B2

公开(公告)日：2009-09-01

申请号：US11349793

申请日：2006-02-08

申请人： Zhuowen Tu ,  Xiang Zhou ,  Dorin Comaniciu ,  Luca Bogoni ,  Adrian Barbu

发明人： Zhuowen Tu ,  Xiang Zhou ,  Dorin Comaniciu ,  Luca Bogoni ,  Adrian Barbu

IPC分类号： G06K9/00

CPC分类号： G06K9/4638 ,  G06K9/38 ,  G06K2209/05 ,  G06T7/11 ,  G06T2200/04 ,  G06T2207/10081 ,  G06T2207/20081 ,  G06T2207/20156 ,  G06T2207/30028

摘要： A system and method for using learned discriminative models to segment a border of an anatomical structure in a three dimensional (3D) image is disclosed. A discriminative probability model is computed for each voxel in the 3D image. Thresholding is performed on each discriminative probability model. One or more two dimensional (2D) slices of the thresholded 3D image along X-Y planes are obtained. Seed regions are selected in the 2D slices. Morphological region growing is performed on the selected seed regions. An initial 3D segmentation is obtained. Boundary evolution is performed on the initial 3D segmentation. The segmented anatomical structure is removed. in the original 3D image.

摘要翻译： 公开了一种用于使用学习的鉴别模型来分割三维（3D）图像中的解剖结构的边界的系统和方法。 为3D图像中的每个体素计算辨别概率模型。 对每个识别概率模型进行阈值处理。 获得沿X-Y平面的阈值3D图像的一个或多个二维（2D）切片。 在2D切片中选择种子区域。 在选择的种子区域上进行形态区域生长。 获得初始3D分割。 对初始3D分割进行边界演化。 切除分割的解剖结构。 在原始的3D图像。

公开(公告)号：US20060239552A1

公开(公告)日：2006-10-26

申请号：US11349793

申请日：2006-02-08

申请人： Zhuowen Tu ,  Xiang Zhou ,  Dorin Comaniciu ,  Luca Bogoni ,  Adrian Barbu

发明人： Zhuowen Tu ,  Xiang Zhou ,  Dorin Comaniciu ,  Luca Bogoni ,  Adrian Barbu

IPC分类号： G06K9/34

CPC分类号： G06K9/4638 ,  G06K9/38 ,  G06K2209/05 ,  G06T7/11 ,  G06T2200/04 ,  G06T2207/10081 ,  G06T2207/20081 ,  G06T2207/20156 ,  G06T2207/30028

摘要： A system and method for using learned discriminative models to segment a border of an anatomical structure in a three dimensional (3D) image is disclosed. A discriminative probability model is computed for each voxel in the 3D image. Thresholding is performed on each discriminative probability model. One or more two dimensional (2D) slices of the thresholded 3D image along X-Y planes are obtained. Seed regions are selected in the 2D slices. Morphological region growing is performed on the selected seed regions. An initial 3D segmentation is obtained. Boundary evolution is performed on the initial 3D segmentation. The segmented anatomical structure is removed. in the original 3D image.

公开(公告)号：US20060079761A1

公开(公告)日：2006-04-13

申请号：US11244798

申请日：2005-10-06

申请人： Zhuowen Tu ,  Xiang Zhou ,  Adrian Barbu ,  Luca Bogoni ,  Dorin Comaniciu

发明人： Zhuowen Tu ,  Xiang Zhou ,  Adrian Barbu ,  Luca Bogoni ,  Dorin Comaniciu

IPC分类号： A61B5/05 ,  G06K9/00

CPC分类号： G06K9/4614 ,  G06K9/6206 ,  G06K9/6214 ,  G06K9/6257 ,  G06K2209/053 ,  G06T7/0012 ,  G06T2207/10072 ,  G06T2207/30032

摘要： A method for detecting target objects in a three dimensional (3D) image volume of an anatomical structure is disclosed. A set of candidate locations in the image volume are obtained. For each candidate location, sub-volumes of at least two different scales are cropped out. Each sub-volume comprises a plurality of voxels. For each of the sub-volumes, each sub-volume is rotated in at least two different orientations. A shape classifier is applied to each sub-volume. If the voxels in the sub-volume pass the shape classifier, a gradient direction is computed for the voxels. If the gradient direction for the voxels is one of a predefined orientation, a probability classifier is applied to the voxels. A probability measure computed by the probability classifier as a confidence measure is used for the sub-volume. If the confidence measure is above a predetermined threshold value, the sub-volume is determined to contain the target object.

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。

公开(公告)号：US07783097B2

公开(公告)日：2010-08-24

申请号：US11733287

申请日：2007-04-10

申请人： Adrian Barbu ,  Luca Bogoni ,  Dorin Comaniciu

发明人： Adrian Barbu ,  Luca Bogoni ,  Dorin Comaniciu

IPC分类号： G06K9/00 ,  G06K9/32

CPC分类号： G06K9/6256 ,  G06K9/6282 ,  G06K2209/051

摘要： The present invention is directed to a system and method for populating a database with a set of image sequences of an object. The database is used to detect a tubular structure in the object. A set of images of objects are received in which each image is annotated to show a tubular structure. For each given image, a Probabilistic Boosting Tree (PBT) is used to detect three dimensional (3D) circles. Short tubes are constructed from pairs of approximately aligned 3D circles. A discriminative joint shape and appearance model is used to classify each short tube. A long flexible tube is formed by connecting all of the short tubes. A tubular structure model that comprises a start point, end point and the long flexible tube is identified. The tubular structure model is stored in the database.

摘要翻译： 本发明涉及一种用于用对象的一组图像序列填充数据库的系统和方法。 数据库用于检测对象中的管状结构。 接收一组对象的图像，其中每个图像被注释以显示管状结构。 对于每个给定的图像，使用概率增强树（PBT）来检测三维（3D）圆。 短管由大约对齐的三维圆对构成。 使用歧视关节形状和外观模型对每根短管进行分类。 通过连接所有短管形成长的柔性管。 识别包括起点，终点和长柔性管的管状结构模型。 管状结构模型存储在数据库中。

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。

公开(公告)号：US20080044071A1

公开(公告)日：2008-02-21

申请号：US11733287

申请日：2007-04-10

申请人： Adrian Barbu ,  Luca Bogoni ,  Dorin Comaniciu

发明人： Adrian Barbu ,  Luca Bogoni ,  Dorin Comaniciu

IPC分类号： G06K9/00

CPC分类号： G06K9/6256 ,  G06K9/6282 ,  G06K2209/051

摘要： The present invention is directed to a system and method for populating a database with a set of image sequences of an object. The database is used to detect a tubular structure in the object. A set of images of objects are received in which each image is annotated to show a tubular structure. For each given image, a Probabilistic Boosting Tree (PBT) is used to detect three dimensional (3D) circles. Short tubes are constructed from pairs of approximately aligned 3D circles. A discriminative joint shape and appearance model is used to classify each short tube. A long flexible tube is formed by connecting all of the short tubes. A tubular structure model that comprises a start point, end point and the long flexible tube is identified. The tubular structure model is stored in the database.

摘要翻译： 本发明涉及一种用于用对象的一组图像序列填充数据库的系统和方法。 数据库用于检测对象中的管状结构。 接收一组对象的图像，其中每个图像被注释以显示管状结构。 对于每个给定的图像，使用概率增强树（PBT）来检测三维（3D）圆。 短管由大约对齐的三维圆对构成。 使用歧视关节形状和外观模型对每根短管进行分类。 通过连接所有短管形成长的柔性管。 识别包括起点，终点和长柔性管的管状结构模型。 管状结构模型存储在数据库中。

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。

公开(公告)号：US08126244B2

公开(公告)日：2012-02-28

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： G06K9/34

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。