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1. 发明申请

US20080085044A1 Method and System For Regression-Based Object Detection in Medical Images 有权
标题翻译：医学图像中基于回归的物体检测方法与系统
公开(公告)号：US20080085044A1
公开(公告)日：2008-04-10
申请号：US11866572
申请日：2007-10-03
申请人： Shaohua Zhou , Jinghao Zhou , Bogdan Georgescu , Dorin Comaniciu
发明人： Shaohua Zhou , Jinghao Zhou , Bogdan Georgescu , Dorin Comaniciu
IPC分类号： G06K9/00
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。

2. 发明授权

US07949173B2 Method and system for regression-based object detection in medical images 有权
标题翻译：医学图像中基于回归的物体检测方法与系统
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。

3. 发明申请

US20070237370A1 System and Method For Using A Similarity Function To Perform Appearance Matching In Image Pairs 有权
标题翻译：在图像对中使用相似性函数进行外观匹配的系统和方法
公开(公告)号：US20070237370A1
公开(公告)日：2007-10-11
申请号：US11539989
申请日：2006-10-10
申请人： Shaohua Zhou , Jie Shao , Jonathan Dowdall , Adrian Barbu , Bogdan Georgescu , Dorin Comaniciu
发明人： Shaohua Zhou , Jie Shao , Jonathan Dowdall , Adrian Barbu , Bogdan Georgescu , Dorin Comaniciu
IPC分类号： G06K9/00
CPC分类号： G06K9/6257 , G06K2209/05 , G06T7/33 , G06T2207/10124 , G06T2207/10132 , G06T2207/30048
摘要： The present invention is directed to a method for populating a database with a set of images of an anatomical structure. The database is used to perform appearance matching in image pairs of the anatomical structure. A set of image pairs of anatomical structures is received, where each image pair is annotated with a plurality of location-sensitive regions that identify a particular aspect of the anatomical structure. Weak learners are iteratively selected and an image patch is identified. A boosting process is used to identify a strong classifier based on responses to the weak learners applied to the identified image patch for each image pair. The responses comprise a feature response and a location response associated with the image patch. Positive and negative image pairs are generated. The positive and negative image pairs are used to learn a similarity function. The learned similarity function and iteratively selected weak learners are stored in the database.
摘要翻译：本发明涉及一种用解剖结构的一组图像填充数据库的方法。该数据库用于在解剖结构的图像对中执行外观匹配。接收一组解剖结构的图像对，其中每个图像对用多个识别解剖结构的特定方面的位置敏感区域注释。迭代选择弱学习者，并识别图像补丁。基于对应用于每个图像对的所识别的图像补丁的弱学习者的响应，使用增强过程来识别强分类器。响应包括与图像块相关联的特征响应和位置响应。产生正负图像对。正负图像对用于学习相似度函数。学习的相似度函数和迭代选择的弱学习者存储在数据库中。

4. 发明申请

US20070071313A1 Method for performing image based regression using boosting 有权
标题翻译：使用升压进行基于图像的回归的方法
公开(公告)号：US20070071313A1
公开(公告)日：2007-03-29
申请号：US11372782
申请日：2006-03-10
申请人： Shaohua Zhou , Bogdan Georgescu , Xiang Zhou , Dorin Comaniciu
发明人： Shaohua Zhou , Bogdan Georgescu , Xiang Zhou , Dorin Comaniciu
IPC分类号： G06K9/62
CPC分类号： G06K9/6256 , G06K2009/00322 , G06K2209/05
摘要： A method for performing image based regression using boosting to infer an entity that is associated with an image of an object is disclosed. A regression function for a plurality of images is learned in which for each image the associated entity is known. The learned regression function is used to predict an entity associated with an image in which the entity is not known.
摘要翻译：公开了一种使用增强来执行基于图像的回归的方法来推断与对象的图像相关联的实体。学习用于多个图像的回归函数，其中对于每个图像，相关联的实体是已知的。学习的回归函数用于预测与实体不知道的图像相关联的实体。

5. 发明申请

US20080107302A1 Fast Detection of Left Ventricle and Its Configuration in 2D/3D Echocardiogram Using Probabilistic Boosting Network 有权
标题翻译：使用概率增强网络快速检测左心室及其在2D / 3D超声心动图中的配置
公开(公告)号：US20080107302A1
公开(公告)日：2008-05-08
申请号：US11872972
申请日：2007-10-16
申请人： Shaohua Zhou , Jingdan Zhang , Bogdan Georgescu , Dorin Comaniciu
发明人： Shaohua Zhou , Jingdan Zhang , Bogdan Georgescu , Dorin Comaniciu
IPC分类号： G06K9/00
CPC分类号： G06K9/6256 , G06K9/00201 , G06K9/3233 , G06K2209/05 , G06T7/73 , G06T2207/30048
摘要： A method for detecting an object of interest in an input image includes the computer-implemented steps of: receiving an image, providing a multi-class pose classifier that identifies a plurality of pose features for estimating a pose of the object of interest, providing a plurality of cascades of serially-linked binary object feature classifiers, each cascade corresponding to different poses of the object of interest in the input image, selecting at least one of the cascades using the estimated pose, and employing the selected cascades to detect instances of the object of interest in the image.
摘要翻译：一种用于在输入图像中检测感兴趣对象的方法包括以下计算机实现的步骤：接收图像，提供识别用于估计感兴趣对象的姿态的多个姿势特征的多类姿势分类器，提供多个级联的串联二进制对象特征分类器，每个级联对应于输入图像中感兴趣对象的不同姿态，使用估计姿态选择级联中的至少一个，并且采用所选择的级联来检测图像中感兴趣的对象。

6. 发明申请

US20080086053A1 Component-Based Approach For Fast Left Ventricle Detection 审中-公开
标题翻译：基于组件的快速左心室检测方法
公开(公告)号：US20080086053A1
公开(公告)日：2008-04-10
申请号：US11866712
申请日：2007-10-03
申请人： Shaohua Zhou , Jingdan Zhang , Bogdan Georgescu , Dorin Comaniciu
发明人： Shaohua Zhou , Jingdan Zhang , Bogdan Georgescu , Dorin Comaniciu
IPC分类号： A61B8/00
CPC分类号： A61B8/08 , A61B6/503
摘要： A method for estimating a configuration of an internal structure within a medical image includes detecting a location of the internal structure. Component-based identification is performed within the detected location of the internal structure to identify a plurality of components. The configuration of the internal structure is estimated based on the relative position of the identified components.
摘要翻译：用于估计医学图像内部结构的配置的方法包括检测内部结构的位置。在检测到的内部结构的位置内执行基于组件的识别以识别多个组件。基于所识别的组件的相对位置来估计内部结构的配置。

7. 发明申请

US20070055153A1 Medical diagnostic imaging optimization based on anatomy recognition 有权
标题翻译：基于解剖识别的医学诊断成像优化
公开(公告)号：US20070055153A1
公开(公告)日：2007-03-08
申请号：US11513675
申请日：2006-08-30
申请人： Constantine Simopoulos , Shaohua Zhou , Jin-hyeong Park , Dorin Comaniciu , Bogdan Georgescu
发明人： Constantine Simopoulos , Shaohua Zhou , Jin-hyeong Park , Dorin Comaniciu , Bogdan Georgescu
IPC分类号： A61B8/00
CPC分类号： A61B8/00 , A61B6/503 , A61B8/0883 , G06F19/00 , G06F19/321 , G16H50/20
摘要： Anatomical information is identified from a medical image and/or used for controlling a medical diagnostic imaging system, such as an ultrasound system. To identify anatomical information from a medical image, a processor applies a multi-class classifier. The anatomical information is used to set an imaging parameter of the medical imaging system. The setting or identification may be used in combination or separately.
摘要翻译：解剖信息从医学图像识别和/或用于控制诸如超声系统的医学诊断成像系统。为了从医学图像中识别解剖信息，处理器应用多类分类器。解剖信息用于设置医学成像系统的成像参数。设置或标识可以组合使用或分开使用。

8. 发明申请

US20070046696A1 SYSTEM AND METHOD FOR LEARNING RELATIVE DISTANCE IN A SHAPE SPACE USING IMAGE BASED FEATURES 有权
标题翻译：使用基于图像的特征在形状空间中学习相对距离的系统和方法
公开(公告)号：US20070046696A1
公开(公告)日：2007-03-01
申请号：US11464851
申请日：2006-08-16
申请人： Yefeng Zheng , Xiang Zhou , Shaohua Zhou , Bogdan Georgescu , Dorin Comaniciu
发明人： Yefeng Zheng , Xiang Zhou , Shaohua Zhou , Bogdan Georgescu , Dorin Comaniciu
IPC分类号： G09G5/00
CPC分类号： G06K9/6255 , G06K9/6206 , G06K9/6256
摘要： A system and method for identifying a shape of an anatomical structure in an input image is disclosed. An input image is received and warped using a set of warping templates resulting in a set of warped images. An integral image is calculated for each warped image. Selected features are extracted based on the integral image. A boosted feature score is calculated for the combined selected features for each warped image. The warped images are ranked based on the boosted feature scores. A predetermined number of warped images are selected that have the largest feature scores. Each selected warped image is associated with its corresponding warping template. The corresponding warping templates are associated with stored shape models. The shape of the input image is identified based on the weighted average of the shapes models.
摘要翻译：公开了一种用于识别输入图像中的解剖结构的形状的系统和方法。使用一组翘曲模板接收和扭曲输入图像，产生一组翘曲图像。为每个弯曲图像计算整体图像。基于积分图像提取所选特征。对于每个弯曲图像的组合选定特征，计算提升的特征分数。翘曲的图像根据提升的特征得分进行排名。选择具有最大特征分数的预定数量的翘曲图像。每个选择的变形图像与其相应的翘曲模板相关联。相应的变形模板与存储的形状模型相关联。基于形状模型的加权平均值来识别输入图像的形状。

9. 发明申请

US20070098239A1 METHOD FOR CHARACTERIZING SHAPE, APPEARANCE AND MOTION OF AN OBJECT THAT IS BEING TRACKED 有权
标题翻译：用于表征正在跟踪的对象的形状，外观和运动的方法
公开(公告)号：US20070098239A1
公开(公告)日：2007-05-03
申请号：US11466477
申请日：2006-08-23
申请人： Shaohua Zhou , Jie Shao , Dorin Comaniciu , Bogdan Georgescu
发明人： Shaohua Zhou , Jie Shao , Dorin Comaniciu , Bogdan Georgescu
IPC分类号： G06K9/00
CPC分类号： G06K9/621 , G06K9/00335 , G06K9/48 , G06K9/6209 , G06K2009/487 , G06K2209/05 , G06T7/251 , G06T2207/10132 , G06T2207/30048
摘要： A method for generating Pairwise Active Appearance Models (PAAMs) that characterize shape, appearance and motion of an object and using the PAAM to track the motion of an object is disclosed. A plurality of video streams is received. Each video stream includes a series of image frames that depict an object in motion. Each video stream includes an index of identified motion phases that are associated with a motion cycle of the object. For each video stream, a shape of the object is represented by a shape vector. An appearance of an object is represented by an appearance vector. The shape and appearance vectors associated at two consecutive motion phases are concatenated. Paired data for the concatenated shape and appearance vectors is computed. Paired data is computed for each two consecutive motion phases in the motion cycle. A shape subspace is constructed based on the computed paired data. An appearance subspace is constructed based on the computed paired data. A joint subspace is constructed using a combination of the shape subspace and appearance subspace. A PAAM is generated using the joint subspace and the PAAM is stored in a database.
摘要翻译：公开了一种用于产生表征对象的形状，外观和运动以及使用PAAM跟踪对象的运动的成对活动外观模型（PAAM）的方法。接收多个视频流。每个视频流包括描绘运动对象的一系列图像帧。每个视频流包括与对象的运动周期相关联的所识别的运动相位的索引。对于每个视频流，对象的形状由形状向量表示。物体的外观由外观矢量表示。在两个连续的运动阶段相关联的形状和外观矢量被连接起来。计算连续形状和外观向量的配对数据。计算运动周期中每两个连续运动相位的配对数据。基于计算的配对数据构建形状子空间。基于计算出的配对数据构建出现子空间。使用形状子空间和外观子空间的组合构建关节子空间。使用联合子空间生成PAAM，并将PAAM存储在数据库中。

10. 发明授权

US10325686B2 System and methods for integrated and predictive analysis of molecular, imaging, and clinical data for patient-specific management of diseases 有权
公开(公告)号：US10325686B2
公开(公告)日：2019-06-18
申请号：US13928430
申请日：2013-06-27
申请人： Tommaso Mansi , Wei Keat Lim , Vanessa King , Andreas Kremer , Bogdan Georgescu , Xudong Zheng , Ali Kamen , Andreas Keller , Cord Friedrich Staehler , Emil Wirsz , Dorin Comaniciu
发明人： Tommaso Mansi , Wei Keat Lim , Vanessa King , Andreas Kremer , Bogdan Georgescu , Xudong Zheng , Ali Kamen , Andreas Keller , Cord Friedrich Staehler , Emil Wirsz , Dorin Comaniciu
IPC分类号： G16H50/50 , G16B5/00
摘要： A system operating in a plurality of modes to provide an integrated analysis of molecular data, imaging data, and clinical data associated with a patient includes a multi-scale model, a molecular model, and a linking component. The multi-scale model is configured to generate one or more estimated multi-scale parameters based on the clinical data and the imaging data when the system operates in a first mode, and generate a model of organ functionality based on one or more inferred multi-scale parameters when the system operates in a second mode. The molecular model is configured to generate one or more first molecular findings based on a molecular network analysis of the molecular data, wherein the molecular model is constrained by the estimated parameters when the system operates in the first mode. The linking component, which is operably coupled to the multi-scale model and the molecular model, is configured to transfer the estimated multi-scale parameters from the multi-scale model to the molecular model when the system operates in the first mode, and generate, using a machine learning process, the inferred multi-scale parameters based on the molecular findings when the system operates in the second mode.

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