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1.发明授权Method and system for brain tumor segmentation in 3D magnetic resonance images 有权3D磁共振图像脑肿瘤分割的方法和系统公开(公告)号：US08280133B2
公开(公告)日：2012-10-02
申请号：US12506674
申请日：2009-07-21
IPC分类号： G06K9/00
CPC分类号： G06K9/469 , G06K9/4614 , G06K9/6257 , G06K9/6297 , G06K2209/05 , G06T7/11 , G06T7/143 , G06T7/162 , G06T2207/10088 , G06T2207/30016
摘要： A method and system for brain tumor segmentation in multi-spectral 3D MRI images is disclosed. A trained probabilistic boosting tree (PBT) classifier is used to determine, for each voxel in a multi-spectral 3D MR image sequence, a probability that the voxel is part of a brain tumor. The brain tumor is then segmented in the multi-spectral 3D MRI image sequence using graph cuts segmentation based on the probabilities determined using the trained PBT classifier and intensities of the voxels in the multi-spectral 3D MR image sequence.
摘要翻译： 公开了一种用于多光谱3D MRI图像中脑肿瘤分割的方法和系统。 训练后的概率增强树（PBT）分类器用于为多光谱3D MR图像序列中的每个体素确定体素是脑肿瘤一部分的概率。 然后使用基于使用经训练的PBT分类器确定的概率和多光谱3D MR图像序列中的体素的强度的图切割分割，在多光谱3D MRI图像序列中将脑肿瘤分割。
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2.发明申请Method and System for Segmentation of Brain Structures in 3D Magnetic Resonance Images 有权三维磁共振图像中脑结构分割的方法与系统公开(公告)号：US20100074499A1
公开(公告)日：2010-03-25
申请号：US12558736
申请日：2009-09-14
申请人： Michael Wels , Gustavo Henrique Monteiro de Barros Carneiro , Martin Huber , Dorin Comaniciu , Yefeng Zheng
发明人： Michael Wels , Gustavo Henrique Monteiro de Barros Carneiro , Martin Huber , Dorin Comaniciu , Yefeng Zheng
IPC分类号： G06T7/00
CPC分类号： G06T7/143 , G06T7/11 , G06T2207/10088 , G06T2207/30016
摘要： A method and system for segmenting multiple brain structures in 3D magnetic resonance (MR) images is disclosed. After intensity standardization of a 3D MR image, a meta-structure including center positions of multiple brain structures is detected in the 3D MR image. The brain structures are then individually segmented using marginal space learning (MSL) constrained by the detected meta-structure.
摘要翻译： 公开了一种在3D磁共振（MR）图像中分割多个脑结构的方法和系统。 在3D MR图像的强度标准化之后，在3D MR图像中检测到包括多个脑结构的中心位置的元结构。 然后使用由检测到的元结构约束的边缘空间学习（MSL）将大脑结构单独分割。
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3.发明申请Method and System for Brain Tumor Segmentation in 3D Magnetic Resonance Images 有权三维磁共振图像中脑肿瘤分割的方法与系统公开(公告)号：US20100027865A1
公开(公告)日：2010-02-04
申请号：US12506674
申请日：2009-07-21
IPC分类号： G06K9/00
CPC分类号： G06K9/469 , G06K9/4614 , G06K9/6257 , G06K9/6297 , G06K2209/05 , G06T7/11 , G06T7/143 , G06T7/162 , G06T2207/10088 , G06T2207/30016
摘要： A method and system for brain tumor segmentation in multi-spectral 3D MRI images is disclosed. A trained probabilistic boosting tree (PBT) classifier is used to determine, for each voxel in a multi-spectral 3D MR image sequence, a probability that the voxel is part of a brain tumor. The brain tumor is then segmented in the multi-spectral 3D MRI image sequence using graph cuts segmentation based on the probabilities determined using the trained PBT classifier and intensities of the voxels in the multi-spectral 3D MR image sequence.
摘要翻译： 公开了一种用于多光谱3D MRI图像中脑肿瘤分割的方法和系统。 训练后的概率增强树（PBT）分类器用于为多光谱3D MR图像序列中的每个体素确定体素是脑肿瘤一部分的概率。 然后使用基于使用经训练的PBT分类器确定的概率和多光谱3D MR图像序列中的体素的强度的图切割分割，在多光谱3D MRI图像序列中将脑肿瘤分割。
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公开(公告)号：US09805473B2
公开(公告)日：2017-10-31
申请号：US12558736
申请日：2009-09-14
申请人： Michael Wels , Gustavo Henrique Monteiro de Barros Carneiro , Martin Huber , Dorin Comaniciu , Yefeng Zheng
发明人： Michael Wels , Gustavo Henrique Monteiro de Barros Carneiro , Martin Huber , Dorin Comaniciu , Yefeng Zheng
CPC分类号： G06T7/143 , G06T7/11 , G06T2207/10088 , G06T2207/30016
摘要： A method and system for segmenting multiple brain structures in 3D magnetic resonance (MR) images is disclosed. After intensity standardization of a 3D MR image, a meta-structure including center positions of multiple brain structures is detected in the 3D MR image. The brain structures are then individually segmented using marginal space learning (MSL) constrained by the detected meta-structure.
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5.发明授权Method and system for segmentation of the prostate in 3D magnetic resonance images 有权3D磁共振图像前列腺分割方法与系统公开(公告)号：US09025841B2
公开(公告)日：2015-05-05
申请号：US12946909
申请日：2010-11-16
申请人： Michael Wels , Michael Suehling , Michael Kelm , Sascha Seifert , Maria Jimena Costa , Alexander Cavallaro , Martin Huber , Dorin Comaniciu
发明人： Michael Wels , Michael Suehling , Michael Kelm , Sascha Seifert , Maria Jimena Costa , Alexander Cavallaro , Martin Huber , Dorin Comaniciu
CPC分类号： G06K9/6207 , G06T7/0012 , G06T7/12 , G06T7/149 , G06T2207/10081 , G06T2207/10088 , G06T2207/30081
摘要： A method and system for fully automatic segmentation the prostate in multi-spectral 3D magnetic resonance (MR) image data having one or more scalar intensity values per voxel is disclosed. After intensity standardization of multi-spectral 3D MR image data, a prostate boundary is detected in the multi-spectral 3D MR image data using marginal space learning (MSL). The detected prostate boundary is refined using one or more trained boundary detectors. The detected prostate boundary can be split into patches corresponding to anatomical regions of the prostate and the detected prostate boundary can be refined using trained boundary detectors corresponding to the patches.
摘要翻译： 公开了一种用于在具有每个体素具有一个或多个标量强度值的多光谱3D磁共振（MR）图像数据中全息自动分割前列腺的方法和系统。 在多光谱3D MR图像数据的强度标准化之后，使用边缘空间学习（MSL）在多光谱3D MR图像数据中检测前列腺边界。 使用一个或多个训练有素的边界检测器来精确检测前列腺边界。 检测到的前列腺边界可以分裂成对应于前列腺的解剖区域的斑块，并且可以使用对应于斑块的训练边界检测器来精细检测检测到的前列腺边界。
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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.发明申请Method and System for Hierarchical Parsing and Semantic Navigation of Full Body Computed Tomography Data 有权全身计算机断层扫描数据的分层解析和语义导航方法与系统公开(公告)号：US20100080434A1
公开(公告)日：2010-04-01
申请号：US12567197
申请日：2009-09-25
申请人： Sascha Seifert , Adrian Barbu , Shaohua Kevin Zhou , David Liu , Johannes Feulner , Martin Huber , Michael Suehling , Alexander Cavallaro , Dorin Comaniciu
发明人： Sascha Seifert , Adrian Barbu , Shaohua Kevin Zhou , David Liu , Johannes Feulner , Martin Huber , Michael Suehling , Alexander Cavallaro , Dorin Comaniciu
IPC分类号： G06K9/00
CPC分类号： G06K9/4638 , G06K2209/05 , G06T7/11 , G06T7/143 , G06T2207/10081 , G06T2207/20081 , G06T2207/20128 , G06T2207/30048 , G06T2207/30056
摘要： A method and apparatus for hierarchical parsing and semantic navigation of a full or partial body computed tomography CT scan is disclosed. In particular, organs are segmented and anatomic landmarks are detected in a full or partial body CT volume. One or more predetermined slices of the CT volume are detected. A plurality of anatomic landmarks and organ centers are then detected in the CT volume using a discriminative anatomical network, each detected in a portion of the CT volume constrained by at least one of the detected slices. A plurality of organs, such as heart, liver, kidneys, spleen, bladder, and prostate, are detected in a sense of a bounding box and segmented in the CT volume, detection of each organ bounding box constrained by the detected organ centers and anatomic landmarks. Organ segmentation is via a database-guided segmentation method.
摘要翻译： 公开了一种用于全身或部分身体计算机断层摄影CT扫描的分层解析和语义导航的方法和装置。 特别地，器官被分割，并且在全身或部分体CT体积中检测解剖标志物。 检测CT体积的一个或多个预定切片。 然后使用辨别性解剖网络在CT体积中检测多个解剖标志和器官中心，每个解剖学网络在CT体积的一部分中被至少一个检测到的切片所约束。 检测多个器官，如心脏，肝脏，肾脏，脾脏，膀胱和前列腺，并在CT体积中被分割，并在被检测到的器官中枢和解剖学上受到限制的每个器官界限盒的检测 地标 器官分割是通过数据库引导的分割方法。
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8.发明授权Method and system for dynamic pulmonary trunk modeling and intervention planning 失效动态肺动脉造影和干预计划方法与系统公开(公告)号：US08538109B2
公开(公告)日：2013-09-17
申请号：US12724612
申请日：2010-03-16
IPC分类号： G06T7/00
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。
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9.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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10.发明申请Method and System for Dynamic Pulmonary Trunk Modeling and Intervention Planning 失效动态肺干模型和干预规划方法与系统公开(公告)号：US20100239147A1
公开(公告)日：2010-09-23
申请号：US12724612
申请日：2010-03-16
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。
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