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1.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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2.发明申请Method and System for Segmentation of the Prostate in 3D Magnetic Resonance Images 有权三维磁共振图像前列腺分割方法与系统公开(公告)号：US20110116698A1
公开(公告)日：2011-05-19
申请号：US12946909
申请日：2010-11-16
申请人： Michael Weis , Michael Suehling , Michael Kelm , Sascha Seifert , Maria Jimena Costa , Alexander Cavallaro , Martin Huber , Dorin Comaniciu
发明人： Michael Weis , Michael Suehling , Michael Kelm , Sascha Seifert , Maria Jimena Costa , Alexander Cavallaro , Martin Huber , Dorin Comaniciu
IPC分类号： G06K9/00
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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公开(公告)号：US09545238B2
公开(公告)日：2017-01-17
申请号：US13053263
申请日：2011-03-22
申请人： Rüdiger Bertsch , Roland Brill , Alexander Cavallaro , Maria Jimena Costa , Martin Huber , Michael Kelm , Helmut König , Sascha Seifert , Michael Wels
发明人： Rüdiger Bertsch , Roland Brill , Alexander Cavallaro , Maria Jimena Costa , Martin Huber , Michael Kelm , Helmut König , Sascha Seifert , Michael Wels
CPC分类号： A61B6/5217 , A61B5/055 , A61B5/415 , A61B5/418 , A61B5/4842 , G06F19/00 , G06F19/321 , G16H50/20
摘要： A method and system for the diagnosis of 3D images are disclosed, which significantly cuts the time required for the diagnosis. The 3D images are for example an image volume dataset of a magnetic resonance tomography system which is saved in an RIS or PACS system. In at least one embodiment, the diagnostic finding are partially automatically generated, and details of the position, size and change in pathological structures are compared to previous diagnostic findings are generated automatically. As a result of this automation the diagnostic work of radiologists is significantly reduced.
摘要翻译： 公开了用于诊断3D图像的方法和系统，其显着地缩短了诊断所需的时间。 3D图像例如是保存在RIS或PACS系统中的磁共振断层摄影系统的图像体积数据集。 在至少一个实施例中，部分地自动生成诊断发现，并且自动地生成病理结构的位置，大小和变化的细节与先前的诊断结果进行比较。 由于这种自动化，放射科医师的诊断工作显着减少。
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公开(公告)号：US20110235887A1
公开(公告)日：2011-09-29
申请号：US13053263
申请日：2011-03-22
申请人： Rüdiger Bertsch , Roland Brill , Alexander Cavallaro , Maria Jimena Costa , Martin Huber , Michael Kelm , Helmut König , Sascha Seifert , Michael Wels
发明人： Rüdiger Bertsch , Roland Brill , Alexander Cavallaro , Maria Jimena Costa , Martin Huber , Michael Kelm , Helmut König , Sascha Seifert , Michael Wels
IPC分类号： G06K9/00
CPC分类号： A61B6/5217 , A61B5/055 , A61B5/415 , A61B5/418 , A61B5/4842 , G06F19/00 , G06F19/321 , G16H50/20
摘要： A method and system for the diagnosis of 3D images are disclosed, which significantly cuts the time required for the diagnosis. The 3D images are for example an image volume dataset of a magnetic resonance tomography system which is saved in an RIS or PACS system. In at least one embodiment, the diagnostic finding are partially automatically generated, and details of the position, size and change in pathological structures are compared to previous diagnostic findings are generated automatically. As a result of this automation the diagnostic work of radiologists is significantly reduced.
摘要翻译： 公开了用于诊断3D图像的方法和系统，其显着地缩短了诊断所需的时间。 3D图像例如是保存在RIS或PACS系统中的磁共振断层摄影系统的图像体积数据集。 在至少一个实施例中，部分地自动生成诊断发现，并且自动地生成病理结构的位置，大小和变化的细节与先前的诊断结果进行比较。 由于这种自动化，放射科医师的诊断工作显着减少。
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5.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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公开(公告)号：US09785858B2
公开(公告)日：2017-10-10
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
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.
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7.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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8.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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9.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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