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11.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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12.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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13.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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14.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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15.发明授权Method for characterizing shape, appearance and motion of an object that is being tracked 有权描述被跟踪物体的形状，外观和运动的方法公开(公告)号：US07620205B2
公开(公告)日：2009-11-17
申请号：US11466477
申请日：2006-08-23
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。
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16.发明授权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基于学习的辨别距离函数进行医学决策支持的方法和系统公开(公告)号：US08812431B2
公开(公告)日：2014-08-19
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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17.发明授权Method and system for automatic detection and classification of coronary stenoses in cardiac CT volumes 有权心脏CT体积冠状动脉狭窄自动检测和分类方法及系统公开(公告)号：US08526699B2
公开(公告)日：2013-09-03
申请号：US13040716
申请日：2011-03-04
申请人： Sushil Mittal , Yefeng Zheng , Bogdan Georgescu , Fernando Vega-Higuera , Shaohua Kevin Zhou , Dorin Comaniciu , Michael Kelm , Alexey Tsymbal , Dominik Bernhardt
发明人： Sushil Mittal , Yefeng Zheng , Bogdan Georgescu , Fernando Vega-Higuera , Shaohua Kevin Zhou , Dorin Comaniciu , Michael Kelm , Alexey Tsymbal , Dominik Bernhardt
CPC分类号： G06T7/0016 , G06T7/143 , G06T2207/10081 , G06T2207/20081 , G06T2207/30101 , G06T2207/30172
摘要： A method and system for providing detecting and classifying coronary stenoses in 3D CT image data is disclosed. Centerlines of coronary vessels are extracted from the CT image data. Non-vessel regions are detected and removed from the coronary vessel centerlines. The cross-section area of the lumen is estimated based on the coronary vessel centerlines using a trained regression function. Stenosis candidates are detected in the coronary vessels based on the estimated lumen cross-section area, and the significant stenosis candidates are automatically classified as calcified, non-calcified, or mixed.
摘要翻译： 公开了一种在3D CT图像数据中提供冠状动脉狭窄检测和分类的方法和系统。 从CT图像数据中提取冠脉血管的中心线。 从血管中心线检测和移除非血管区域。 使用经过训练的回归函数，基于冠状血管中心线估计管腔的横截面面积。 基于估计的管腔横截面积在冠状血管中检测狭窄候选物，并且显着狭窄候选物被自动分类为钙化，非钙化或混合。
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18.发明申请Method and System for Automatic Detection and Classification of Coronary Stenoses in Cardiac CT Volumes 有权心脏CT卷冠状动脉狭窄自动检测与分类方法与系统公开(公告)号：US20110224542A1
公开(公告)日：2011-09-15
申请号：US13040716
申请日：2011-03-04
申请人： Sushil Mittal , Yefeng Zheng , Bogdan Georgescu , Fernando Vega-Higuera , Shaohua Kevin Zhou , Dorin Comaniciu , Michael Kelm , Alexey Tsymbal , Dominik Bernhardt
发明人： Sushil Mittal , Yefeng Zheng , Bogdan Georgescu , Fernando Vega-Higuera , Shaohua Kevin Zhou , Dorin Comaniciu , Michael Kelm , Alexey Tsymbal , Dominik Bernhardt
IPC分类号： A61B6/03
CPC分类号： G06T7/0016 , G06T7/143 , G06T2207/10081 , G06T2207/20081 , G06T2207/30101 , G06T2207/30172
摘要： A method and system for providing detecting and classifying coronary stenoses in 3D CT image data is disclosed. Centerlines of coronary vessels are extracted from the CT image data. Non-vessel regions are detected and removed from the coronary vessel centerlines. The cross-section area of the lumen is estimated based on the coronary vessel centerlines using a trained regression function. Stenosis candidates are detected in the coronary vessels based on the estimated lumen cross-section area, and the significant stenosis candidates are automatically classified as calcified, non-calcified, or mixed.
摘要翻译： 公开了一种在3D CT图像数据中提供冠状动脉狭窄检测和分类的方法和系统。 从CT图像数据中提取冠脉血管的中心线。 从血管中心线检测和移除非血管区域。 使用经过训练的回归函数，基于冠状血管中心线估计管腔的横截面面积。 基于估计的管腔横截面积在冠状血管中检测狭窄候选物，并且显着狭窄候选物被自动分类为钙化，非钙化或混合。
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19.发明授权Fast detection of left ventricle and its configuration in 2D/3D echocardiogram using probabilistic boosting network 有权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：US08000497B2
公开(公告)日：2011-08-16
申请号：US11872972
申请日：2007-10-16
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。
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20.发明授权公开(公告)号：US08073215B2
公开(公告)日：2011-12-06
申请号：US12186815
申请日：2008-08-06
申请人： Xiaoguang Lu , Bogdan Georgescu , Yefeng Zheng , Joanne Otsuki , Dorin Comaniciu
发明人： Xiaoguang Lu , Bogdan Georgescu , Yefeng Zheng , Joanne Otsuki , Dorin Comaniciu
IPC分类号： G06K9/00
CPC分类号： A61B8/00 , A61B8/06 , A61B8/065 , A61B8/08 , A61B8/0883 , A61B8/0891 , A61B8/13 , A61B8/14 , A61B8/463 , A61B8/483 , A61B8/488 , A61B8/523 , G06F19/321 , G06K9/468 , G06K9/6256 , G16H50/20
摘要： A plane position for a standard view is detected from three-dimensional echocardiographic data. The position of the plane within the volume is defined by translation, orientation (rotation), and/or scale. Possible positions are detected and other possible positions are ruled out. The classification of the possible positions occurs sequentially by translation, then orientation, and then scale. The sequential process may limit calculations required to identify the plane position for a desired view.
摘要翻译： 从三维超声心动图数据检测标准视图的平面位置。 平面在体积内的位置由平移，定向（旋转）和/或刻度来定义。 检测到可能的位置，排除其他可能的位置。 可能的位置的分类依次通过翻译，然后定向，然后缩放。 顺序过程可能会限制为所需视图识别平面位置所需的计算。
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