公开(公告)号：US20120134564A1

公开(公告)日：2012-05-31

申请号：US13295217

申请日：2011-11-14

申请人： Yefeng Zheng ,  Fernando Vega-Higuera ,  Dorin Comaniciu

发明人： Yefeng Zheng ,  Fernando Vega-Higuera ,  Dorin Comaniciu

IPC分类号： G06K9/00

CPC分类号： G06T7/149 ,  G06T7/11 ,  G06T2207/10081 ,  G06T2207/20124 ,  G06T2207/30048

摘要： A method and system for isolating the heart in a 3D volume, such as a cardiac CT volume, for patients with coronary artery bypasses is disclosed. An initial heart isolation mask is extracted from a 3D volume, such as a cardiac CT volume. The aortic root and ascending aorta are segmented in the 3D volume, resulting in an aorta mesh. The aorta mesh is expanded to include bypass coronary arteries. An expanded heart isolation mask is generated by combining the initial heart isolation mask with an expanded aorta mask defined by the expanded aorta mesh.

摘要翻译： 公开了一种用于分离冠状动脉旁路患者的3D体积（例如心脏CT体积）中的心脏的方法和系统。 从3D体积（例如心脏CT体积）提取初始心脏隔离掩模。 主动脉根部和升主动脉在3D体积中分段，导致主动脉网。 主动脉网扩张包括旁路冠状动脉。 通过将初始心脏隔离掩模与由扩张的主动脉网所定义的扩张主动脉掩膜组合来产生扩张的心脏隔离掩模。

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：US08150116B2

公开(公告)日：2012-04-03

申请号：US12214339

申请日：2008-06-18

申请人： Shaohua Kevin Zhou ,  Feng Guo ,  Jin-hyeong Park ,  Gustavo Henrique Monteiro de Barros Carneiro ,  Constantine Simopoulos ,  Joanne Otsuki ,  Dorin Comaniciu ,  John I. Jackson

发明人： Shaohua Kevin Zhou ,  Feng Guo ,  Jin-hyeong Park ,  Gustavo Henrique Monteiro de Barros Carneiro ,  Constantine Simopoulos ,  Joanne Otsuki ,  Dorin Comaniciu ,  John I. Jackson

IPC分类号： G06K17/00

CPC分类号： G06K9/0055 ,  G06K9/6206 ,  G06K9/6256

摘要： A method and system for detection of deformable structures in medical images is disclosed. Deformable structures can represent blood flow patterns in images such as Doppler echocardiograms. A probabilistic, hierarchical, and discriminant framework is used to detect such deformable structures. This framework integrates evidence from different primitive levels via a progressive detector hierarchy, including a series of discriminant classifiers. A target deformable structure is parameterized by a multi-dimensional parameter, and primitives or partial parameterizations of the parameter are determined. An input image is received, and a series of primitives are sequentially detected using the progressive detector hierarchy, in which each detector or classifier detects a corresponding primitive. The final detector detects configuration candidates for the deformable structure.

摘要翻译： 公开了用于检测医学图像中的可变形结构的方法和系统。 可变形结构可以表示图像中的血流模式，例如多普勒超声心动图。 概率，分层和判别框架用于检测这种可变形结构。 该框架通过渐进式检测器层次结合不同原始级别的证据，包括一系列判别分类器。 目标可变形结构通过多维参数进行参数化，并确定参数的基元或部分参数化。 接收输入图像，并且使用逐行检测器层级顺序地检测一系列图元，其中每个检测器或分类器检测相应的图元。 最终检测器检测可变形结构的配置候选。

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。

公开(公告)号：US08068654B2

公开(公告)日：2011-11-29

申请号：US12012386

申请日：2008-02-01

申请人： Adrian Barbu ,  Le Lu ,  Luca Bogoni ,  Marcos Salganicoff ,  Dorin Comaniciu

发明人： Adrian Barbu ,  Le Lu ,  Luca Bogoni ,  Marcos Salganicoff ,  Dorin Comaniciu

IPC分类号： G06K9/00 ,  G06T15/00

CPC分类号： G06K9/32 ,  G06K9/00201 ,  G06K9/6256 ,  G06K2209/051

摘要： A method and system for detecting 3D objects in images is disclosed. In particular, a method and system for Ileo-Cecal Valve detection in 3D computed tomography (CT) images using incremental parameter learning and ICV specific prior learning is disclosed. First, second, and third classifiers are sequentially trained to detect candidates for position, scale, and orientation parameters of a box that bounds an object in 3D image. In the training of each sequential classifier, new training samples are generated by scanning the object's configuration parameters in the current learning projected subspace (position, scale, orientation), based on detected candidates resulting from the previous training step. This allows simultaneous detection and registration of a 3D object with full 9 degrees of freedom. ICV specific prior learning can be used to detect candidate voxels for an orifice of the ICV and to detect initial ICV box candidates using a constrained orientation alignment at each candidate voxel.

摘要翻译： 公开了一种用于检测图像中的3D物体的方法和系统。 特别地，公开了使用增量参数学习和ICV特有的先前学习的3D计算机断层摄影（CT）图像中Ileo-Cecal Valve检测的方法和系统。 顺序训练第一，第二和第三分类器以检测在3D图像中界定对象的框的位置，缩放和取向参数的候选。 在每个顺序分类器的训练中，基于从先前的训练步骤得到的检测到的候选，通过在当前学习投影子空间（位置，比例，方向）中扫描对象的配置参数来生成新的训练样本。 这允许同时检测和注册具有全9自由度的3D对象。 ICV具体的先验学习可用于检测ICV孔口的候选体素，并使用每个候选体素上的约束取向对齐来检测初始ICV盒候选。

公开(公告)号：US08060178B2

公开(公告)日：2011-11-15

申请号：US12243199

申请日：2008-10-01

申请人： Xiang Zhou ,  Dorin Comaniciu ,  Alok Gupta ,  Visvanathan Ramesh ,  Bhavani Duggirala ,  Diane Paine

发明人： Xiang Zhou ,  Dorin Comaniciu ,  Alok Gupta ,  Visvanathan Ramesh ,  Bhavani Duggirala ,  Diane Paine

IPC分类号： A61B5/00

CPC分类号： G06K9/6276 ,  A61B8/0883 ,  G06F19/00 ,  G06K9/6256 ,  G06K2209/05 ,  G06Q50/22 ,  G16H50/20 ,  Y10S128/92 ,  Y10S128/924

摘要： A system and method for providing decision support to a physician during a medical examination is disclosed. Data is received from a sensor representing a particular medical measurement. The received data includes image data. The received data and context data is analyzed with respect to one or more sets of training models. Probability values for the particular medical measurement and other measurements to be taken are derived based on the analysis and based on identified classes. The received image data is compared with training images. Distance values are determined between the received image data and the training images, and the training images are associated with the identified classes. Absolute value feature sensitivity scores are derived for the particular medical measurement and other measurements to be taken based on the analysis. The probability values, distance values and absolute value feature sensitivity scores are outputted to the user.

摘要翻译： 公开了一种用于在医学检查期间向医师提供决策支持的系统和方法。 从代表特定医疗测量的传感器接收数据。 所接收的数据包括图像数据。 相对于一组或多组训练模型分析接收到的数据和上下文数据。 特定医疗测量和其他测量的概率值基于分析并基于识别的类别导出。 将接收到的图像数据与训练图像进行比较。 在接收的图像数据和训练图像之间确定距离值，并且训练图像与所识别的类别相关联。 基于分析，针对特定医疗测量和其他测量得出绝对值特征灵敏度得分。 将概率值，距离值和绝对值特征灵敏度得分输出给用户。

公开(公告)号：US08009900B2

公开(公告)日：2011-08-30

申请号：US11856208

申请日：2007-09-17

申请人： Adrian Barbu ,  Yefeng Zheng ,  Jing Yang ,  Bogdan Georgescu ,  Dorin Comaniciu

发明人： Adrian Barbu ,  Yefeng Zheng ,  Jing Yang ,  Bogdan Georgescu ,  Dorin Comaniciu

IPC分类号： G06K9/00 ,  G06K9/62

CPC分类号： G06K9/6292

摘要： A system and method for detecting an object in a high dimensional image space is disclosed. A three dimensional image of an object is received. A first classifier is trained in the marginal space of the object center location which generates a predetermined number of candidate object center locations. A second classifier is trained to identify potential object center locations and orientations from the predetermined number of candidate object center locations and maintaining a subset of the candidate object center locations. A third classifier is trained to identify potential locations, orientations and scale of the object center from the subset of the candidate object center locations. A single candidate object pose for the object is identified.

摘要翻译： 公开了一种用于检测高维图像空间中的对象的系统和方法。 接收对象的三维图像。 在对象中心位置的边缘空间中训练第一分类器，其生成预定数量的候选对象中心位置。 训练第二分类器以从预定数量的候选对象中心位置识别潜在对象中心位置和方向，并维护候选对象中心位置的子集。 训练第三分类器以从候选对象中心位置的子集中识别对象中心的潜在位置，方向和尺度。 识别对象的单个候选对象姿势。

公开(公告)号：US07995820B2

公开(公告)日：2011-08-09

申请号：US12056107

申请日：2008-03-26

申请人： Gustavo Henrique Monteiro de Barros Carneiro ,  Bogdan Georgescu ,  Sara Good ,  Dorin Comaniciu

发明人： Gustavo Henrique Monteiro de Barros Carneiro ,  Bogdan Georgescu ,  Sara Good ,  Dorin Comaniciu

IPC分类号： G06K9/00

CPC分类号： G06K9/46 ,  A61B8/0866 ,  G06K9/3233 ,  G06K9/38 ,  G06K9/4614 ,  G06K9/6282 ,  G06K2009/366 ,  G06K2209/051

摘要： A method for detecting fetal anatomic features in ultrasound images includes providing an ultrasound image of a fetus, specifying an anatomic feature to be detected in a region S determined by parameter vector θ, providing a sequence of probabilistic boosting tree classifiers, each with a pre-specified height and number of nodes. Each classifier computes a posterior probability P(y|S) where yε{−1,+1}, with P(y=+1|S) representing a probability that region S contains the feature, and P(y=−1|S) representing a probability that region S contains background information. The feature is detected by uniformly sampling a parameter space of parameter vector θ using a first classifier with a sampling interval vector used for training said first classifier, and having each subsequent classifier classify positive samples identified by a preceding classifier using a smaller sampling interval vector used for training said preceding classifier. Each classifier forms a union of its positive samples with those of the preceding classifier.

摘要翻译： 一种用于检测超声图像中的胎儿解剖特征的方法，包括提供胎儿的超声图像，指定在由参数矢量和姿势确定的区域S中要检测的解剖特征;提供一系列概率增强树分类器，每个具有预先 指定的高度和节点数。 每个分类器计算出一个后验概率P（y | S），其中y（e）= { - 1，+ 1}，其中P（y = + 1 | S）表示区域S包含特征的概率，P（y = -1 | S），表示区域S包含背景信息的概率。 通过对参数矢量和参数的参数空间进行均匀采样来检测该特征; 使用具有用于训练所述第一分类器的采样间隔向量的第一分类器，并且每个后续分类器使用用于训练所述先前分类器的较小采样间隔向量来对由先前分类器标识的正样本进行分类。 每个分类器形成其正样本与上一分类器的并集。

公开(公告)号：US07949173B2

公开(公告)日：2011-05-24

申请号：US11866572

申请日：2007-10-03

申请人： Shaohua Kevin Zhou ,  Jinghao Zhou ,  Bogdan Georgescu ,  Dorin Comaniciu

发明人： Shaohua Kevin Zhou ,  Jinghao Zhou ,  Bogdan Georgescu ,  Dorin Comaniciu

IPC分类号： G06K9/00 ,  A61B6/00

CPC分类号： G06K9/00 ,  G06K9/6256 ,  G06K9/72 ,  G06K2209/05

摘要： A method and system for regression-based object detection in medical images is disclosed. A regression function for predicting a location of an object in a medical image based on an image patch is trained using image-based boosting ridge regression (IBRR). The trained regression function is used to determine a difference vector based on an image patch of a medical image. The difference vector represents the difference between the location of the image patch and the location of a target object. The location of the target object in the medical image is predicted based on the difference vector determined by the regression function.

摘要翻译： 公开了一种用于医学图像中基于回归的物体检测的方法和系统。 使用基于图像的增强脊回归（IBRR）训练用于基于图像块预测医学图像中的对象的位置的回归函数。 训练回归函数用于基于医学图像的图像块来确定差分矢量。 差分向量表示图像块的位置与目标对象的位置之间的差异。 基于由回归函数确定的差分向量来预测目标对象在医学图像中的位置。

公开(公告)号：US20110096969A1

公开(公告)日：2011-04-28

申请号：US12877168

申请日：2010-09-08

申请人： Yefeng Zheng ,  Matthias John ,  Jan Boese ,  Dorin Comaniciu

发明人： Yefeng Zheng ,  Matthias John ,  Jan Boese ,  Dorin Comaniciu

IPC分类号： G06K9/00

CPC分类号： G06K9/00214 ,  A61B6/032 ,  A61B6/4441 ,  A61B6/503 ,  A61B6/504 ,  A61B6/5211 ,  G06K9/6209

摘要： A system and method for performing shape-constrained aortic valve landmark detection using 3D medical images is provided. A rigid global shape defining initial positions of a plurality of aortic valve landmarks is detected within a 3D image. Each of the plurality of aortic valve landmarks is detected based on the initial positions.

摘要翻译： 提供了一种用于使用3D医学图像进行形状约束的主动脉瓣地标检测的系统和方法。 在3D图像内检测限定多个主动脉瓣地标的初始位置的刚性全局形状。 基于初始位置检测多个主动脉瓣标记中的每一个。