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1.发明申请Method and System for Detecting 3D Anatomical Structures Using Constrained Marginal Space Learning 有权使用约束边际空间学习检测3D解剖结构的方法和系统公开(公告)号：US20090304251A1
公开(公告)日：2009-12-10
申请号：US12471761
申请日：2009-05-26
IPC分类号： G06K9/00
CPC分类号： G06K9/3233 , G06K2209/051 , G06T7/75 , G06T2207/10081 , G06T2207/30004
摘要： A method and apparatus for detecting 3D anatomical objects in medical images using constrained marginal space learning (MSL) is disclosed. A constrained search range is determined for an input medical image volume based on training data. A first trained classifier is used to detect position candidates in the constrained search range. Position-orientation hypotheses are generated from the position candidates using orientation examples in the training data. A second trained classifier is used to detect position-orientation candidates from the position-orientation hypotheses. Similarity transformation hypotheses are generated from the position-orientation candidates based on scale examples in the training data. A third trained classifier is used to detect similarity transformation candidates from the similarity transformation hypotheses, and the similarity transformation candidates define the position, translation, and scale of the 3D anatomic object in the medical image volume.
摘要翻译： 公开了一种使用受限边际空间学习（MSL）检测医学图像中3D解剖学对象的方法和装置。 基于训练数据确定输入医学图像体积的约束搜索范围。 第一训练分类器用于检测约束搜索范围内的位置候选。 使用训练数据中的取向示例从位置候选者生成位置取向假设。 第二训练分类器用于从位置定向假设检测位置方向候选。 基于训练数据中的比例示例，从位置定位候选生成相似度转换假设。 第三训练分类器用于从相似变换假设检测相似变换候选，并且相似变换候选定义医学图像体积中的3D解剖对象的位置，平移和比例。
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2.发明授权Method and system for detecting 3D anatomical structures using constrained marginal space learning 有权使用约束边际空间学习检测3D解剖结构的方法和系统公开(公告)号：US08116548B2
公开(公告)日：2012-02-14
申请号：US12471761
申请日：2009-05-26
IPC分类号： G06K9/00
CPC分类号： G06K9/3233 , G06K2209/051 , G06T7/75 , G06T2207/10081 , G06T2207/30004
摘要： A method and apparatus for detecting 3D anatomical objects in medical images using constrained marginal space learning (MSL) is disclosed. A constrained search range is determined for an input medical image volume based on training data. A first trained classifier is used to detect position candidates in the constrained search range. Position-orientation hypotheses are generated from the position candidates using orientation examples in the training data. A second trained classifier is used to detect position-orientation candidates from the position-orientation hypotheses. Similarity transformation hypotheses are generated from the position-orientation candidates based on scale examples in the training data. A third trained classifier is used to detect similarity transformation candidates from the similarity transformation hypotheses, and the similarity transformation candidates define the position, translation, and scale of the 3D anatomic object in the medical image volume.
摘要翻译： 公开了一种使用受限边际空间学习（MSL）检测医学图像中3D解剖学对象的方法和装置。 基于训练数据确定输入医学图像体积的约束搜索范围。 第一训练分类器用于检测约束搜索范围内的位置候选。 使用训练数据中的取向示例从位置候选者生成位置取向假设。 第二训练分类器用于从位置定向假设检测位置方向候选。 基于训练数据中的比例示例，从位置定位候选生成相似度转换假设。 第三训练分类器用于从相似变换假设检测相似变换候选，并且相似变换候选定义医学图像体积中的3D解剖对象的位置，平移和比例。
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公开(公告)号：US20090080745A1
公开(公告)日：2009-03-26
申请号：US12228911
申请日：2008-08-18
IPC分类号： G06K9/00
CPC分类号： G06T7/62 , G06T7/11 , G06T2207/10081 , G06T2207/30048
摘要： A method and system for measuring the volume of the left ventricle (LV) in a 3D medical image, such as a CT, volume is disclosed. Heart chambers are segmented in the CT volume, including at least the LV endocardium and the LV epicardium. An optimal threshold value is automatically determined based on voxel intensities within the LV endocardium and voxel intensities between the LV endocardium and the LV epicardium. Voxels within the LV endocardium are labeled as blood pool voxels or papillary muscle voxels based on the optimal threshold value. The LV volume can be measured excluding the papillary muscles based on the number of blood pool voxels, and the LV volume can be measured including the papillary muscles based on the total number of voxels within the LV endocardium.
摘要翻译： 公开了一种用于测量3D医学图像（例如CT）体积中的左心室（LV）的体积的方法和系统。 心室在CT体积中分段，至少包括LV心内膜和LV心外膜。 基于LV心内膜内的体素强度和LV心内膜与LV心外膜之间的体素强度自动确定最佳阈值。 基于最佳阈值将LV心内膜内的体素标记为血池体素或乳头肌肉体素。 基于血液池体素的数量可以除去乳头肌之外的LV体积，并且可以基于LV心内膜中的体素总数来测量LV体积，包括乳头肌。
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4.发明授权Method and system for left ventricle endocardium surface segmentation using constrained optimal mesh smoothing 失效使用约束最优网格平滑的左心室心内膜表面分割的方法和系统公开(公告)号：US08150119B2
公开(公告)日：2012-04-03
申请号：US12319686
申请日：2009-01-09
CPC分类号： G06K9/34 , G06K2209/051 , G06T7/12 , G06T7/149 , G06T2207/10081 , G06T2207/20124 , G06T2207/30048
摘要： A method and system for left ventricle (LV) endocardium surface segmentation using constrained optimal mesh smoothing is disclosed. The LV endocardium surface in the 3D cardiac volume is initially segmented in a 3D cardiac volume, such as a CT volume, resulting in an LV endocardium surface mesh. A smoothed LV endocardium surface mesh is generated by smoothing the LV endocardium surface mesh using constrained optimal mesh smoothing. The constrained optimal mesh smoothing determines an optimal adjustment for each point on the LV endocardium surface mesh by minimizing an objective function based at least on a smoothness measure, subject to a constraint bounding the adjustment for each point. The adjustment for each point can be constrained to prevent adjustments inward toward the blood pool in order to ensure that the smoothed LV endocardium surface mesh encloses the entire blood pool.
摘要翻译： 公开了使用约束最优网格平滑的左心室（LV）心内膜表面分割的方法和系统。 3D心脏体积中的LV心内膜表面最初在3D心脏体积（例如CT体积）中分段，导致LV心内膜表面网。 通过使用约束最优网格平滑平滑LV心内膜表面网格来生成平滑的LV心内膜表面网格。 受约束的最优网格平滑通过使至少基于平滑度量度的目标函数最小化来限制LV心内膜表面网格上的每个点的最佳调整，受限于对每个点的调整。 可以限制每个点的调整以防止向血液向内的调节，以确保平滑的LV心内膜表面网格包围整个血液池。
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公开(公告)号：US20080262814A1
公开(公告)日：2008-10-23
申请号：US12082143
申请日：2008-04-09
申请人： Yefeng Zheng , Adrian Barbu , Bogdan Georgescu , Michael Lynch , Michael Scheuering , Dorin Comaniciu
发明人： Yefeng Zheng , Adrian Barbu , Bogdan Georgescu , Michael Lynch , Michael Scheuering , Dorin Comaniciu
IPC分类号： G06G7/60
CPC分类号： G06F19/3437 , G06F19/00 , G06F19/34 , G06T2207/30048 , G16H50/50
摘要： A method and system for building a statistical four-chamber heart model from 3D volumes is disclosed. In order to generate the four-chamber heart model, each chamber is modeled using an open mesh, with holes at the valves. Based on the image data in one or more 3D volumes, meshes are generated and edited for the left ventricle (LV), left atrium (LA), right ventricle (RV), and right atrium (RA). Resampling to enforce point correspondence is performed during mesh editing. Important anatomic landmarks in the heart are explicitly represented in the four-chamber heart model of the present invention.
摘要翻译： 公开了一种用于从3D体积构建统计四室心脏模型的方法和系统。 为了产生四腔心脏模型，每个腔室都使用开放的网格进行建模，在阀门处有孔。 基于一个或多个3D体积中的图像数据，为左心室（LV），左心房（LA），右心室（RV）和右心房（RA）生成并编辑网格。 在网格编辑期间执行重新采样以强制点对应。 在本发明的四腔心脏模型中明确地表示心脏中的重要解剖学标志。
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公开(公告)号：US08098918B2
公开(公告)日：2012-01-17
申请号：US12228911
申请日：2008-08-18
IPC分类号： G06K9/00
CPC分类号： G06T7/62 , G06T7/11 , G06T2207/10081 , G06T2207/30048
摘要： A method and system for measuring the volume of the left ventricle (LV) in a 3D medical image, such as a CT, volume is disclosed. Heart chambers are segmented in the CT volume, including at least the LV endocardium and the LV epicardium. An optimal threshold value is automatically determined based on voxel intensities within the LV endocardium and voxel intensities between the LV endocardium and the LV epicardium. Voxels within the LV endocardium are labeled as blood pool voxels or papillary muscle voxels based on the optimal threshold value. The LV volume can be measured excluding the papillary muscles based on the number of blood pool voxels, and the LV volume can be measured including the papillary muscles based on the total number of voxels within the LV endocardium.
摘要翻译： 公开了一种用于测量3D医学图像（例如CT）体积中的左心室（LV）的体积的方法和系统。 心室在CT体积中分段，至少包括LV心内膜和LV心外膜。 基于LV心内膜内的体素强度和LV心内膜与LV心外膜之间的体素强度自动确定最佳阈值。 基于最佳阈值将LV心内膜内的体素标记为血池体素或乳头肌肉体素。 基于血液池体素的数量可以除去乳头肌之外的LV体积，并且可以基于LV心内膜中的体素总数来测量LV体积，包括乳头肌。
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7.发明授权System and method for segmenting chambers of a heart in a three dimensional image 有权用于在三维图像中分割心脏室的系统和方法公开(公告)号：US07916919B2
公开(公告)日：2011-03-29
申请号：US11856878
申请日：2007-09-18
IPC分类号： G06K9/00
CPC分类号： A61B6/503 , A61B8/0883 , G06K9/6247 , G06T7/12 , G06T7/149 , G06T2207/10081 , G06T2207/30048
摘要： A system and method for segmenting chambers of a heart in three dimensional images is disclosed. A set of three dimensional images of a heart is received. The shape of the heart in the three dimensional images is localized. Boundaries of the chambers of the heart in the localized shape are identified using steerable features.
摘要翻译： 公开了一种用于在三维图像中分割心脏腔室的系统和方法。 接收一组心脏的三维图像。 三维图像中心脏的形状是局部化的。 使用可转向特征来识别局部形状的心脏腔室的边界。
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8.发明申请Method and system for left ventricle endocardium surface segmentation using constrained optimal mesh smoothing 失效使用约束最优网格平滑的左心室心内膜表面分割的方法和系统公开(公告)号：US20090190811A1
公开(公告)日：2009-07-30
申请号：US12319686
申请日：2009-01-09
CPC分类号： G06K9/34 , G06K2209/051 , G06T7/12 , G06T7/149 , G06T2207/10081 , G06T2207/20124 , G06T2207/30048
摘要： A method and system for left ventricle (LV) endocardium surface segmentation using constrained optimal mesh smoothing is disclosed. The LV endocardium surface in the 3D cardiac volume is initially segmented in a 3D cardiac volume, such as a CT volume, resulting in an LV endocardium surface mesh. A smoothed LV endocardium surface mesh is generated by smoothing the LV endocardium surface mesh using constrained optimal mesh smoothing. The constrained optimal mesh smoothing determines an optimal adjustment for each point on the LV endocardium surface mesh by minimizing an objective function based at least on a smoothness measure, subject to a constraint bounding the adjustment for each point. The adjustment for each point can be constrained to prevent adjustments inward toward the blood pool in order to ensure that the smoothed LV endocardium surface mesh encloses the entire blood pool.
摘要翻译： 公开了使用约束最优网格平滑的左心室（LV）心内膜表面分割的方法和系统。 3D心脏体积中的LV心内膜表面最初在3D心脏体积（例如CT体积）中分段，导致LV心内膜表面网。 通过使用约束最优网格平滑平滑LV心内膜表面网格来生成平滑的LV心内膜表面网格。 受约束的最优网格平滑通过使至少基于平滑度量度的目标函数最小化来限制LV心内膜表面网格上的每个点的最佳调整，受限于对每个点的调整。 可以限制每个点的调整以防止向血液向内的调节，以确保平滑的LV心内膜表面网格包围整个血液池。
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公开(公告)号：US09275190B2
公开(公告)日：2016-03-01
申请号：US12082143
申请日：2008-04-09
申请人： Yefeng Zheng , Adrian Barbu , Bogdan Georgescu , Michael Lynch , Michael Scheuering , Dorin Comaniciu
发明人： Yefeng Zheng , Adrian Barbu , Bogdan Georgescu , Michael Lynch , Michael Scheuering , Dorin Comaniciu
IPC分类号： G06F19/00
CPC分类号： G06F19/3437 , G06F19/00 , G06F19/34 , G06T2207/30048 , G16H50/50
摘要： A method and system for building a statistical four-chamber heart model from 3D volumes is disclosed. In order to generate the four-chamber heart model, each chamber is modeled using an open mesh, with holes at the valves. Based on the image data in one or more 3D volumes, meshes are generated and edited for the left ventricle (LV), left atrium (LA), right ventricle (RV), and right atrium (RA). Resampling to enforce point correspondence is performed during mesh editing. Important anatomic landmarks in the heart are explicitly represented in the four-chamber heart model of the present invention.
摘要翻译： 公开了一种用于从3D体积构建统计四室心脏模型的方法和系统。 为了产生四腔心脏模型，每个腔室都使用开放的网格进行建模，在阀门处有孔。 基于一个或多个3D体积中的图像数据，为左心室（LV），左心房（LA），右心室（RV）和右心房（RA）生成并编辑网格。 在网格编辑期间执行重新采样以强制点对应。 在本发明的四腔心脏模型中明确地表示心脏中的重要解剖学标志。
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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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