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

IPC分类号： G06N5/00 ,  G06G7/60

CPC分类号： G06G7/60 ,  G06N5/00

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。

公开(公告)号：US07970191B2

公开(公告)日：2011-06-28

申请号：US11860591

申请日：2007-09-25

申请人： Bogdan Georgescu ,  Peter Durlak ,  Vassilis Athitsos ,  Adrian Barbu ,  Dorin Comaniciu

发明人： Bogdan Georgescu ,  Peter Durlak ,  Vassilis Athitsos ,  Adrian Barbu ,  Dorin Comaniciu

IPC分类号： G06K9/00 ,  G06K9/40

CPC分类号： G06T5/003 ,  G06T5/20 ,  G06T7/74 ,  G06T2207/10121 ,  G06T2207/20016 ,  G06T2207/30021

摘要： A method for downsampling fluoroscopic images and enhancing guidewire visibility during coronary angioplasty includes providing a first digitized image, filtering the image with one or more steerable filters of different angular orientations, assigning a weight W and orientation O for each pixel based on the filter response for each pixel, wherein each pixel weight is assigned to a function of a maximum filter response magnitude and the pixel orientation is calculated from the angle producing the maximum filter response if the magnitude is greater than zero, wherein guidewire pixels have a higher weight than non-guidewire pixels, and downsampling the orientation and weights to calculate a second image of half the resolution of the first image, wherein the downsampling accounts for the orientation and higher weight assigned to the guidewire pixels.

摘要翻译： 用于在冠状动脉血管成形术期间对荧光透视图像进行下采样并增强导丝线可视性的方法包括提供第一数字化图像，用一个或多个不同角取向的可转向滤光片过滤图像，基于滤波器响应为每个像素分配权重W和取向O 每个像素，其中每个像素权重被分配给最大滤波器响应幅度的函数，并且如果幅度大于零，则从产生最大滤波器响应的角度计算像素取向，其中导丝像素具有比非最大滤波器响应幅度更大的权重， 引导线像素，以及对取向和权重进行下采样以计算第一图像的分辨率的一半的第二图像，其中下采样考虑分配给导丝像素的取向和较高权重。

公开(公告)号：US20100239147A1

公开(公告)日：2010-09-23

申请号：US12724612

申请日：2010-03-16

申请人： Dime Vitanovski ,  Razvan Ioan Ionasec ,  Bogdan Georgescu ,  Martin Huber ,  Dorin Comaniciu

发明人： Dime Vitanovski ,  Razvan Ioan Ionasec ,  Bogdan Georgescu ,  Martin Huber ,  Dorin Comaniciu

IPC分类号： G06K9/00

CPC分类号： G06K9/4638 ,  G06K9/6224 ,  G06K2209/051 ,  G06T7/0012 ,  G06T2207/10076 ,  G06T2207/20081 ,  G06T2207/20108 ,  G06T2207/30048 ,  G06T2207/30101

摘要： A method and system for modeling the pulmonary trunk in 4D image data, such as 4D CT data, and model-based percutaneous pulmonary valve implantation (PPVI) intervention is disclosed. A patient-specific dynamic pulmonary trunk data is generated from 4D image data of a patient. The patient is automatically classified as suitable for PPVI intervention or not suitable for PPVI intervention based on the generated patient-specific dynamic pulmonary trunk model.

摘要翻译： 公开了4D图像数据（如4D CT数据）和基于模型的经皮肺动脉瓣植入（PPVI）干预对肺动脉干线进行建模的方法和系统。 从患者的4D图像数据生成患者特异的动态肺动脉干数据。 根据生成的患者特异性动态肺动脉模型，患者自动分类为适合PPVI干预或不适合PPVI干预。

公开(公告)号：US07747054B2

公开(公告)日：2010-06-29

申请号：US12401899

申请日：2009-03-11

申请人： Xiang Zhou ,  Bogdan Georgescu ,  Dorin Comaniciu ,  R. Bharat Rao ,  Alok Gupta

发明人： Xiang Zhou ,  Bogdan Georgescu ,  Dorin Comaniciu ,  R. Bharat Rao ,  Alok Gupta

IPC分类号： G06K9/00

CPC分类号： G06K9/6206 ,  G06K9/6256 ,  G06T7/12 ,  G06T7/149 ,  G06T7/251 ,  G06T2207/10132 ,  G06T2207/30048

摘要： A detection framework that matches anatomical structures using appearance and shape is disclosed. A training set of images are used in which object shapes or structures are annotated in the images. A second training set of images represents negative examples for such shapes and structures, i.e., images containing no such objects or structures. A classification algorithm trained on the training sets is used to detect a structure at its location. The structure is matched to a counterpart in the training set that can provide details about the structure's shape and appearance.

摘要翻译： 公开了一种使用外观和形状匹配解剖结构的检测框架。 使用图像的训练集，其中在图像中注释对象形状或结构。 图像的第二训练集表示这种形状和结构的负面例子，即不包含这样的对象或结构的图像。 训练集训练的分类算法用于检测其位置的结构。 该结构与训练集中的对应物匹配，可以提供关于结构形状和外观的细节。

公开(公告)号：US20100076296A1

公开(公告)日：2010-03-25

申请号：US12487223

申请日：2009-06-18

申请人： Sushil Mittal ,  Yefeng Zheng ,  Bogdan Georgescu ,  Fernando Vega-Higuera ,  Dorin Comaniciu

发明人： Sushil Mittal ,  Yefeng Zheng ,  Bogdan Georgescu ,  Fernando Vega-Higuera ,  Dorin Comaniciu

IPC分类号： A61B5/05

CPC分类号： G06T7/13 ,  G06T7/0012 ,  G06T2207/10081 ,  G06T2207/20044 ,  G06T2207/20081 ,  G06T2207/30048 ,  G06T2207/30101

摘要： A method and system for automatic coronary stenosis detection in computed tomography (CT) data is disclosed. Coronary artery centerlines are obtained in an input cardiac CT volume. A trained classifier, such as a probabilistic boosting tree (PBT) classifier, is used to detect stenosis regions along the centerlines in the input cardiac CT volume. The classifier classifies each of the control points that define the coronary artery centerlines as a stenosis point or a non-stenosis point.

摘要翻译： 公开了一种计算机断层扫描（CT）数据中自动冠状动脉狭窄检测的方法和系统。 在输入的心脏CT体积中获得冠状动脉中心线。 训练有素的分类器，如概率增强树（PBT）分类器，用于检测输入心脏CT体积中沿着中心线的狭窄区域。 分类器将定义冠状动脉中心线的每个控制点分类为狭窄点或非狭窄点。

公开(公告)号：US20100070249A1

公开(公告)日：2010-03-18

申请号：US12562454

申请日：2009-09-18

申请人： Razvan Ionasec ,  Bogdan Georgescu ,  Yefeng Zheng ,  Dorin Comaniciu

发明人： Razvan Ionasec ,  Bogdan Georgescu ,  Yefeng Zheng ,  Dorin Comaniciu

IPC分类号： G06G7/60 ,  G06F7/60 ,  G06F17/10

CPC分类号： G06K9/6256 ,  G06F19/00 ,  G06F19/321 ,  G06K2209/051 ,  G06T7/12 ,  G06T7/149 ,  G06T13/20 ,  G06T17/00 ,  G06T2207/30048 ,  G06T2210/41 ,  G16H50/50

摘要： A method and system for generating a patient specific anatomical heart model is disclosed. Volumetric image data, such as computed tomography (CT) or echocardiography image data, of a patient's cardiac region is received. Individual models for multiple heart components, such as the left ventricle (LV) endocardium, LV epicardium, right ventricle (RV), left atrium (LA), right atrium (RA), mitral valve, aortic valve, aorta, and pulmonary trunk, are estimated in said volumetric cardiac image data. A patient specific anatomical heart model is generated by integrating the individual models for each of the heart components.

摘要翻译： 公开了一种用于产生患者特异性解剖心脏模型的方法和系统。 接收患者心脏区域的体积图像数据，例如计算机断层扫描（CT）或超声心动图像数据。 用于多个心脏组件的单独模型，例如左心室（LV）心内膜，LV心外膜，右心室（RV），左心房（LA），右心房（RA），二尖瓣，主动脉瓣，主动脉和肺动脉干， 在所述体积心脏图像数据中估计。 患者特异性解剖心脏模型是通过对每个心脏部件的各个模型进行整合来产生的。

公开(公告)号：US07603000B2

公开(公告)日：2009-10-13

申请号：US11464851

申请日：2006-08-16

申请人： Yefeng Zheng ,  Xiang Zhou ,  Shaohua Kevin Zhou ,  Bogdan Georgescu ,  Dorin Comaniciu

发明人： Yefeng Zheng ,  Xiang Zhou ,  Shaohua Kevin Zhou ,  Bogdan Georgescu ,  Dorin Comaniciu

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。

公开(公告)号：US07555151B2

公开(公告)日：2009-06-30

申请号：US11218018

申请日：2005-09-01

申请人： Dorin Comaniciu ,  Bogdan Georgescu ,  Jing Xiao ,  Xiang Zhou

发明人： Dorin Comaniciu ,  Bogdan Georgescu ,  Jing Xiao ,  Xiang Zhou

IPC分类号： G06K9/00

CPC分类号： G06K9/3216 ,  A61B8/08 ,  A61B8/0883 ,  A61B8/463 ,  A61B8/483 ,  G06K2209/051 ,  G06T7/0016 ,  G06T7/12 ,  G06T7/149 ,  G06T7/246 ,  G06T7/277 ,  G06T2200/04 ,  G06T2200/24 ,  G06T2207/10136 ,  G06T2207/30048 ,  Y10S128/922

摘要： A system and method for defining and tracking a deformable shape of a candidate anatomical structure wall in a three dimensional (3D) image is disclosed. The shape of the candidate anatomical structure is represented by a plurality of labeled 3D landmark points. At least one 3D landmark point of the deformable shape in an image frame is defined. A 3D cuboid is defined around the detected 3D landmark point. For each landmark point associated with the anatomical structure, its location and location uncertainty matrix is estimated in subsequent frames relative to the reference anatomical structures. A shape model is generated to represent dynamics of the deformable shape in subsequent image frames. The shape model includes statistical information from a training data set of 3D images of representative anatomical structures. The shape model is aligned to the deformable shape of the candidate anatomical structure. The shape model is fused with the deformable shape. A current shape of the candidate anatomical structure is estimated.

摘要翻译： 公开了一种在三维（3D）图像中定义和跟踪候选解剖结构壁的可变形形状的系统和方法。 候选解剖结构的形状由多个标记的3D地标点表示。 定义图像帧中的可变形形状的至少一个3D地标点。 3D立方体围绕检测到的3D地标点定义。 对于与解剖结构相关联的每个界标点，在相对于参考解剖结构的后续帧中估计其位置和位置不确定性矩阵。 生成形状模型以表示后续图像帧中的可变形形状的动态。 形状模型包括来自代表性解剖结构的3D图像的训练数据集的统计信息。 形状模型与候选解剖结构的可变形形状对齐。 形状模型与可变形的形状融合。 估计候选解剖结构的当前形状。

公开(公告)号：US20090123050A1

公开(公告)日：2009-05-14

申请号：US12288215

申请日：2008-10-17

申请人： Razvan Ionasec ,  Bogdan Georgescu ,  Helene C. Houle ,  Sebastian Vogt ,  Michael Scheuering ,  Estelle Camus ,  Dorin Comaniciu

发明人： Razvan Ionasec ,  Bogdan Georgescu ,  Helene C. Houle ,  Sebastian Vogt ,  Michael Scheuering ,  Estelle Camus ,  Dorin Comaniciu

IPC分类号： G06K9/00

CPC分类号： G06K9/00 ,  G06K2209/051 ,  G06T7/0016 ,  G06T7/251 ,  G06T17/20 ,  G06T2207/10076 ,  G06T2207/10136 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/30048

摘要： A method and system for modeling the aortic valve in 4D image data, such as 4D CT and echocardiography, is disclosed. An initial estimate of a physiological aortic valve model is determined for at least one reference frame of a 4D image sequence based on anatomic features in the reference frame. The initial estimate is refined to generate a final estimate in the reference frame. A dynamic model of the aortic valve is then generated by estimating the physiological aortic valve model for each remaining frame of the 4D image sequence based on the final estimate in the reference frame. The aortic valve can be quantitatively evaluated using the dynamic model.

摘要翻译： 公开了用于4D图像数据建模主动脉瓣的方法和系统，如4D CT和超声心动图。 基于参考系中的解剖特征，为4D图像序列的至少一个参考帧确定生理主动脉瓣模型的初始估计。 改进初始估计以在参考系中产生最终估计。 然后通过基于参考框架中的最终估计估计4D图像序列的每个剩余帧的生理主动脉瓣模型来生成主动脉瓣的动态模型。 主动脉瓣可以使用动态模型进行定量评估。

公开(公告)号：US20090080745A1

公开(公告)日：2009-03-26

申请号：US12228911

申请日：2008-08-18

申请人： Yefeng Zheng ,  Bogdan Georgescu ,  Michael Scheuering ,  Dorin Comaniciu

发明人： Yefeng Zheng ,  Bogdan Georgescu ,  Michael Scheuering ,  Dorin Comaniciu

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。