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1.发明授权公开(公告)号：US07949173B2
公开(公告)日：2011-05-24
申请号：US11866572
申请日：2007-10-03
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。
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公开(公告)号：US09824302B2
公开(公告)日：2017-11-21
申请号：US13412869
申请日：2012-03-06
申请人： Sasa Grbic , Razvan Ioan Ionasec , Yang Wang , Bogdan Georgescu , Tommaso Mansi , Dorin Comaniciu , Yefeng Zheng , Shaohua Kevin Zhou , Matthias John , Jan Boese
发明人： Sasa Grbic , Razvan Ioan Ionasec , Yang Wang , Bogdan Georgescu , Tommaso Mansi , Dorin Comaniciu , Yefeng Zheng , Shaohua Kevin Zhou , Matthias John , Jan Boese
CPC分类号： G06K9/6289 , G06K9/6211
摘要： A method and system for fusion of multi-modal volumetric images is disclosed. A first image acquired using a first imaging modality is received. A second image acquired using a second imaging modality is received. A model and of a target anatomical structure and a transformation are jointly estimated from the first and second images. The model represents a model of the target anatomical structure in the first image and the transformation projects a model of the target anatomical structure in the second image to the model in the first image. The first and second images can be fused based on estimated transformation.
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3.发明授权System and method for using a similarity function to perform appearance matching in image pairs 有权使用相似度函数执行图像对中的外观匹配的系统和方法公开(公告)号：US07831074B2
公开(公告)日：2010-11-09
申请号：US11539989
申请日：2006-10-10
申请人： Shaohua Kevin Zhou , Jie Shao , Jonathan Dowdall , Adrian Barbu , Bogdan Georgescu , Dorin Comaniciu
发明人： Shaohua Kevin Zhou , Jie Shao , Jonathan Dowdall , Adrian Barbu , Bogdan Georgescu , Dorin Comaniciu
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。
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公开(公告)号：US07804999B2
公开(公告)日：2010-09-28
申请号：US11372782
申请日：2006-03-10
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。
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5.发明授权Method and system for patient-specific computational modeling and simulation for coupled hemodynamic analysis of cerebral vessels 有权用于患者特异性计算建模和模拟脑血管耦合血流动力学分析的方法和系统公开(公告)号：US09141763B2
公开(公告)日：2015-09-22
申请号：US13366677
申请日：2012-02-06
申请人： Puneet Sharma , Tommaso Mansi , Viorel Mihalef , Jingdan Zhang , David Liu , Shaohua Kevin Zhou , Bogdan Georgescu , Dorin Comaniciu
发明人： Puneet Sharma , Tommaso Mansi , Viorel Mihalef , Jingdan Zhang , David Liu , Shaohua Kevin Zhou , Bogdan Georgescu , Dorin Comaniciu
CPC分类号： G06F19/3437 , G06F19/00 , G16H50/50
摘要： A method and system for patient-specific computational modeling and simulation for coupled hemodynamic analysis of cerebral vessels is disclosed. An anatomical model of a cerebral vessel is extracted from 3D medical image data. The anatomical model of the cerebral vessel includes an inner wall and an outer wall of the cerebral vessel. Blood flow in the cerebral vessel and deformation of the cerebral vessel wall are simulated using coupled computational fluid dynamics (CFD) and computational solid mechanics (CSM) simulations based on the anatomical model of the cerebral vessel.
摘要翻译： 公开了一种用于脑血管耦合血流动力学分析的患者特异性计算建模和模拟方法及系统。 从3D医学图像数据中提取脑血管的解剖模型。 脑血管的解剖模型包括脑血管的内壁和外壁。 基于脑血管解剖模型，使用耦合计算流体动力学（CFD）和计算固体力学（CSM）模拟模拟脑血管中的血流和脑血管壁的变形。
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6.发明申请公开(公告)号：US20120230568A1
公开(公告)日：2012-09-13
申请号：US13412869
申请日：2012-03-06
申请人： Sasa Grbic , Razvan Ioan Ionasec , Yang Wang , Bogdan Georgescu , Tommaso Mansi , Dorin Comaniciu , Yefeng Zheng , Shaohua Kevin Zhou , Matthias John , Jan Boese
发明人： Sasa Grbic , Razvan Ioan Ionasec , Yang Wang , Bogdan Georgescu , Tommaso Mansi , Dorin Comaniciu , Yefeng Zheng , Shaohua Kevin Zhou , Matthias John , Jan Boese
IPC分类号： G06K9/36
CPC分类号： G06K9/6289 , G06K9/6211
摘要： A method and system for fusion of multi-modal volumetric images is disclosed. A first image acquired using a first imaging modality is received. A second image acquired using a second imaging modality is received. A model and of a target anatomical structure and a transformation are jointly estimated from the first and second images. The model represents a model of the target anatomical structure in the first image and the transformation projects a model of the target anatomical structure in the second image to the model in the first image. The first and second images can be fused based on estimated transformation.
摘要翻译： 公开了一种用于融合多模体积图像的方法和系统。 接收使用第一成像模式获取的第一图像。 接收使用第二成像模态获取的第二图像。 从第一和第二图像共同估计模型和目标解剖结构和变换。 该模型表示第一图像中目标解剖结构的模型，并且变换将第二图像中的目标解剖结构的模型投影到第一图像中的模型。 可以基于估计的变换来融合第一和第二图像。
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7.发明申请Method and System for Medical Decision Support Using Organ Models and Learning Based Discriminative Distance Functions 有权使用器官模型和学习型判别距离函数进行医学决策支持的方法与系统公开(公告)号：US20110191283A1
公开(公告)日：2011-08-04
申请号：US13016259
申请日：2011-01-28
申请人： Ingmar Voigt , Dime Vitanovski , Razvan Ioan Ionasec , Alexey Tsymbal , Bogdan Georgescu , Shaohua Kevin Zhou , Martin Huber , Dorin Comaniciu
发明人： Ingmar Voigt , Dime Vitanovski , Razvan Ioan Ionasec , Alexey Tsymbal , Bogdan Georgescu , Shaohua Kevin Zhou , Martin Huber , Dorin Comaniciu
摘要： A method and system for providing medical decision support based on virtual organ models and learning based discriminative distance functions is disclosed. A patient-specific virtual organ model is generated from medical image data of a patient. One or more similar organ models to the patient-specific organ model are retrieved from a plurality of previously stored virtual organ models using a learned discriminative distance function. The patient-specific valve model can be classified into a first class or a second class based on the previously stored organ models determined to be similar to the patient-specific organ model.
摘要翻译： 公开了一种基于虚拟器官模型和基于学习的辨别距离函数提供医学决策支持的方法和系统。 从患者的医学图像数据生成患者特异性虚拟器官模型。 使用学习的鉴别距离函数从多个先前存储的虚拟器官模型检索与患者特异器官模型的一个或多个相似的器官模型。 基于先前存储的被确定为类似于患者特异器官模型的器官模型，患者特异性瓣膜模型可以分为第一类或第二类。
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8.发明授权System and method for learning relative distance in a shape space using image based features 有权使用基于图像的特征来学习形状空间中的相对距离的系统和方法公开(公告)号：US07603000B2
公开(公告)日：2009-10-13
申请号：US11464851
申请日：2006-08-16
IPC分类号： G06K9/60
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。
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公开(公告)号：US07648460B2
公开(公告)日：2010-01-19
申请号：US11513675
申请日：2006-08-30
申请人： Constantine Simopoulos , Shaohua Kevin Zhou , Jin-hyeong Park , Dorin Comaniciu , Bogdan Georgescu
发明人： Constantine Simopoulos , Shaohua Kevin Zhou , Jin-hyeong Park , Dorin Comaniciu , Bogdan Georgescu
IPC分类号： A61B17/00
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。
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10.发明申请Method and System for Patient-Specific Computational Modeling and Simulation for Coupled Hemodynamic Analysis of Cerebral Vessels 有权用于患者特异性计算建模和脑血管耦合血流动力学分析的方法和系统公开(公告)号：US20120203530A1
公开(公告)日：2012-08-09
申请号：US13366677
申请日：2012-02-06
申请人： Puneet Sharma , Tommaso Mansi , Viorel Mihalef , Jingdan Zhang , David Liu , Shaohua Kevin Zhou , Bogdan Georgescu , Dorin Comaniciu
发明人： Puneet Sharma , Tommaso Mansi , Viorel Mihalef , Jingdan Zhang , David Liu , Shaohua Kevin Zhou , Bogdan Georgescu , Dorin Comaniciu
IPC分类号： G06G7/60
CPC分类号： G06F19/3437 , G06F19/00 , G16H50/50
摘要： A method and system for patient-specific computational modeling and simulation for coupled hemodynamic analysis of cerebral vessels is disclosed. An anatomical model of a cerebral vessel is extracted from 3D medical image data. The anatomical model of the cerebral vessel includes an inner wall and an outer wall of the cerebral vessel. Blood flow in the cerebral vessel and deformation of the cerebral vessel wall are simulated using coupled computational fluid dynamics (CFD) and computational solid mechanics (CSM) simulations based on the anatomical model of the cerebral vessel.
摘要翻译： 公开了一种用于脑血管耦合血流动力学分析的患者特异性计算建模和模拟方法和系统。 从3D医学图像数据中提取脑血管的解剖模型。 脑血管的解剖模型包括脑血管的内壁和外壁。 基于脑血管解剖模型，使用耦合计算流体动力学（CFD）和计算固体力学（CSM）模拟模拟脑血管中的血流和脑血管壁的变形。
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