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1.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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2.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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公开(公告)号：US20120321174A1
公开(公告)日：2012-12-20
申请号：US13160744
申请日：2011-06-15
申请人： Alexey Tsymbal , Michael Kelm , Maria Jimena Costa , Shaohua Kevin Zhou , Dorin Comaniciu , Yefeng Zheng , Alexander Schwing
发明人： Alexey Tsymbal , Michael Kelm , Maria Jimena Costa , Shaohua Kevin Zhou , Dorin Comaniciu , Yefeng Zheng , Alexander Schwing
IPC分类号： G06K9/62
CPC分类号： G06K9/6219 , G06F17/30247 , G06F17/30268 , G06K9/6263 , G06K9/6282
摘要： A method of performing image retrieval includes training a random forest RF classifier based on low-level features of training images and a high-level feature, using similarity values generated by the RF classifier to determine a subset of the training images that are most similar to one another, and classifying input images for the high-level feature using the RF classifier and the determined subset of images.
摘要翻译： 执行图像检索的方法包括基于训练图像的低级特征和高级特征来训练随机森林RF分类器，使用由RF分类器生成的相似性值来确定最相似的训练图像的子集 并且使用RF分类器和确定的图像子集对高级特征的输入图像进行分类。
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公开(公告)号：US08744172B2
公开(公告)日：2014-06-03
申请号：US13160744
申请日：2011-06-15
申请人： Alexey Tsymbal , Michael Kelm , Maria Jimena Costa , Shaohua Kevin Zhou , Dorin Comaniciu , Yefeng Zheng , Alexander Schwing
发明人： Alexey Tsymbal , Michael Kelm , Maria Jimena Costa , Shaohua Kevin Zhou , Dorin Comaniciu , Yefeng Zheng , Alexander Schwing
IPC分类号： G06K9/00
CPC分类号： G06K9/6219 , G06F17/30247 , G06F17/30268 , G06K9/6263 , G06K9/6282
摘要： A method of performing image retrieval includes training a random forest RF classifier based on low-level features of training images and a high-level feature, using similarity values generated by the RF classifier to determine a subset of the training images that are most similar to one another, and classifying input images for the high-level feature using the RF classifier and the determined subset of images.
摘要翻译： 执行图像检索的方法包括基于训练图像的低级特征和高级特征来训练随机森林RF分类器，使用由RF分类器生成的相似性值来确定最相似的训练图像的子集 并且使用RF分类器和确定的图像子集对高级特征的输入图像进行分类。
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5.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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6.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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7.发明授权Method and system for regression-based 4D mitral valve segmentation from 2D+t magnetic resonance imaging slices 有权2D + t磁共振成像切片的基于回归的二维二尖瓣分割方法与系统公开(公告)号：US09196049B2
公开(公告)日：2015-11-24
申请号：US13416168
申请日：2012-03-09
申请人： Razvan Ioan Ionasec , Dime Vitanovski , Alexey Tsymbal , Gareth Funka-Lea , Dorin Comaniciu , Andreas Greiser , Edgar Mueller
发明人： Razvan Ioan Ionasec , Dime Vitanovski , Alexey Tsymbal , Gareth Funka-Lea , Dorin Comaniciu , Andreas Greiser , Edgar Mueller
CPC分类号： G06T7/0083 , G06T7/12 , G06T2207/10088 , G06T2207/20076 , G06T2207/20081 , G06T2207/30048
摘要： A system and method for regression-based segmentation of the mitral valve in 2D+t cardiac magnetic resonance (CMR) slices is disclosed. The 2D+t CMR slices are acquired according to a mitral valve-specific acquisition protocol introduced herein. A set of mitral valve landmarks is detected in each 2D CMR slice and mitral valve contours are estimated in each 2D CMR slice based on the detected landmarks. A full mitral valve model is reconstructed from the mitral valve contours estimated in the 2D CMR slices using a trained regression model. Each 2D CMR slice may be a cine image acquired over a full cardiac cycle. In this case, the segmentation method reconstructs a patient-specific 4D dynamic mitral valve model from the 2D+t CMR image data.
摘要翻译： 公开了一种用于2D + t心脏磁共振（CMR）切片二尖瓣回归分割的系统和方法。 根据本文引入的二尖瓣特异性获取方案获取2D + t CMR切片。 在每个2D CMR切片中检测到一组二尖瓣地标，并且基于检测到的界标在每个2D CMR切片中估计二尖瓣轮廓。 使用训练有素的回归模型，从2D CMR切片中估算的二尖瓣轮廓重建完整的二尖瓣模型。 每个2D CMR切片可以是在整个心动周期上获取的电影图像。 在这种情况下，分割方法从2D + t CMR图像数据重建患者特有的4D动态二尖瓣模型。
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8.发明申请Method and System for Regression-Based 4D Mitral Valve Segmentation From 2D+t Magnetic Resonance Imaging Slices 有权从2D + t磁共振成像切片回归基于4D二尖瓣分割的方法和系统公开(公告)号：US20120232379A1
公开(公告)日：2012-09-13
申请号：US13416168
申请日：2012-03-09
申请人： Razvan Ioan Ionasec , Dime Vitanovski , Alexey Tsymbal , Gareth Funka-Lea , Dorin Comaniciu , Andreas Greiser , Edgar Mueller
发明人： Razvan Ioan Ionasec , Dime Vitanovski , Alexey Tsymbal , Gareth Funka-Lea , Dorin Comaniciu , Andreas Greiser , Edgar Mueller
IPC分类号： A61B5/055
CPC分类号： G06T7/0083 , G06T7/12 , G06T2207/10088 , G06T2207/20076 , G06T2207/20081 , G06T2207/30048
摘要： A system and method for regression-based segmentation of the mitral valve in 2D+t cardiac magnetic resonance (CMR) slices is disclosed. The 2D+t CMR slices are acquired according to a mitral valve-specific acquisition protocol introduced herein. A set of mitral valve landmarks is detected in each 2D CMR slice and mitral valve contours are estimated in each 2D CMR slice based on the detected landmarks. A full mitral valve model is reconstructed from the mitral valve contours estimated in the 2D CMR slices using a trained regression model. Each 2D CMR slice may be a cine image acquired over a full cardiac cycle. In this case, the segmentation method reconstructs a patient-specific 4D dynamic mitral valve model from the 2D+t CMR image data.
摘要翻译： 公开了一种用于2D + t心脏磁共振（CMR）切片二尖瓣回归分割的系统和方法。 根据本文引入的二尖瓣特异性获取方案获取2D + t CMR切片。 在每个2D CMR切片中检测到一组二尖瓣地标，并且基于检测到的界标在每个2D CMR切片中估计二尖瓣轮廓。 使用训练有素的回归模型，从2D CMR切片中估算的二尖瓣轮廓重建完整的二尖瓣模型。 每个2D CMR切片可以是在整个心动周期上获取的电影图像。 在这种情况下，分割方法从2D + t CMR图像数据重建患者特定的4D动态二尖瓣模型。
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公开(公告)号：US09792703B2
公开(公告)日：2017-10-17
申请号：US14792259
申请日：2015-07-06
CPC分类号： G06T11/003 , A61B6/025 , A61B6/502 , G06T3/0031 , G06T7/0012 , G06T7/11 , G06T7/62 , G06T11/008 , G06T15/08 , G06T2207/10081 , G06T2207/10088 , G06T2207/10112 , G06T2207/10124 , G06T2207/20064 , G06T2207/30068 , G06T2207/30096 , G06T2210/41 , G06T2211/436
摘要： A method for generating a synthetic two-dimensional mammogram with enhanced contrast for structures of interest includes acquiring a three-dimensional digital breast tomosynthesis volume having a plurality of voxels. A three-dimensional relevance map that encodes for the voxels the relevance of the underlying structure for a diagnosis is generated. A synthetic two-dimensional mammogram is calculated based on the three-dimensional digital breast tomosynthesis volume and the three-dimensional relevance map.
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公开(公告)号：US20170011534A1
公开(公告)日：2017-01-12
申请号：US14792259
申请日：2015-07-06
CPC分类号： G06T11/003 , A61B6/025 , A61B6/502 , G06T3/0031 , G06T7/0012 , G06T7/11 , G06T7/62 , G06T11/008 , G06T15/08 , G06T2207/10081 , G06T2207/10088 , G06T2207/10112 , G06T2207/10124 , G06T2207/20064 , G06T2207/30068 , G06T2207/30096 , G06T2210/41 , G06T2211/436
摘要： A method for generating a synthetic two-dimensional mammogram with enhanced contrast for structures of interest includes acquiring a three-dimensional digital breast tomosynthesis volume having a plurality of voxels. A three-dimensional relevance map that encodes for the voxels the relevance of the underlying structure for a diagnosis is generated. A synthetic two-dimensional mammogram is calculated based on the three-dimensional digital breast tomosynthesis volume and the three-dimensional relevance map.
摘要翻译： 用于产生具有增强的感兴趣结构的对比度的合成二维乳房X线照片的方法包括获取具有多个体素的三维数字乳房断层合成体积。 生成用于对体素进行编码的三维相关性图，用于诊断的底层结构的相关性。 基于三维数字乳房体层合成量和三维相关性图计算合成二维乳房X线照片。
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