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1.发明申请METHOD AND APPARATUS FOR GENERATION OF IMAGE DATA BASED ON MR THERMOMETRY DATA 审中-公开基于MR热成像数据生成图像数据的方法和装置公开(公告)号：US20140243654A1
公开(公告)日：2014-08-28
申请号：US14179860
申请日：2014-02-13
申请人： Joerg Roland , Eva Rothgang , Michael Suehling
发明人： Joerg Roland , Eva Rothgang , Michael Suehling
摘要： In a method, apparatus and medical imaging system to generate image data based on magnetic resonance (MR) thermometry data, planning data of a region of an examination subject that is to be depicted thermometrically are provided to a processor. Through the processor, segmentation data based on the planning data are generated MR thermometry data are provided to the processor, which generates image data on the basis of the MR thermometry data, using the segmentation data.
摘要翻译： 在一种用于基于磁共振（MR）测温数据生成图像数据的方法，装置和医学成像系统中，将要被温度描绘的检查对象的区域的规划数据提供给处理器。 通过处理器，生成基于计划数据的分割数据，MR测温数据被提供给处理器，处理器使用分割数据基于MR测温数据生成图像数据。
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2.发明授权Method and system for detection 3D spinal geometry using iterated marginal space learning 有权使用迭代边缘空间学习检测3D脊柱几何的方法和系统公开(公告)号：US09042618B2
公开(公告)日：2015-05-26
申请号：US12794850
申请日：2010-06-07
CPC分类号： G06T7/0087 , G06K2209/055 , G06T7/143 , G06T2207/20076 , G06T2207/20081 , G06T2207/30012
摘要： A method and apparatus for automatic detection and labeling of 3D spinal geometry is disclosed. Cervical, thoracic, and lumbar spine regions are detected in a 3D image. Intervertebral disk candidates are detected in each of the spine regions using iterative marginal space learning (MSL). Using a global probabilistic spine model, a separate one of the intervertebral disk candidates is selected for each of a plurality of labeled intervertebral disk locations.
摘要翻译： 公开了一种用于3D脊柱几何的自动检测和标记的方法和装置。 在3D图像中检测到子宫颈，胸部和腰椎区域。 使用迭代边缘空间学习（MSL）在每个脊柱区域中检测椎间盘候选物。 使用全局概率脊柱模型，为多个标记的椎间盘位置中的每一个选择椎间盘候选物中的单独的一个。
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3.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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公开(公告)号：US20120014559A1
公开(公告)日：2012-01-19
申请号：US12986212
申请日：2011-01-07
申请人： Michael Suehling , Martin Huber , Grzegorz Soza
发明人： Michael Suehling , Martin Huber , Grzegorz Soza
IPC分类号： G06K9/00
CPC分类号： G06K9/6207 , G06F19/00 , G06F19/321 , G06T7/33 , G06T2207/10081 , G06T2207/10088 , G06T2207/10116 , G06T2207/10132 , G06T2207/30096
摘要： A method and system for automatic semantics driven registration of medical images is disclosed. Anatomic landmarks and organs are detected in a first image and a second image. Pathologies are also detected in the first image and the second image. Semantic information is automatically extracted from text-based documents associated with the first and second images, and the second image is registered to the first image based the detected anatomic landmarks, organs, and pathologies, and the extracted semantic information.
摘要翻译： 公开了一种用于医学图像的自动语义驱动注册的方法和系统。 在第一图像和第二图像中检测解剖标志和器官。 在第一图像和第二图像中还检测病理学。 基于与第一和第二图像相关联的基于文本的文档自动提取语义信息，并且基于检测到的解剖标志，器官和病理以及所提取的语义信息将第二图像登记到第一图像。
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5.发明授权Method and system for automatic detection and segmentation of axillary lymph nodes 有权腋窝淋巴结自动检测和分割的方法和系统公开(公告)号：US08391579B2
公开(公告)日：2013-03-05
申请号：US13041569
申请日：2011-03-07
申请人： Adrian Barbu , Michael Suehling , Xun (Jason) Xu , David Liu , Shaohua Kevin Zhou , Dorin Comaniciu
发明人： Adrian Barbu , Michael Suehling , Xun (Jason) Xu , David Liu , Shaohua Kevin Zhou , Dorin Comaniciu
CPC分类号： G06T7/11 , G06K2209/051 , G06T7/143 , G06T2207/10081 , G06T2207/30004
摘要： A method and system for automatically detecting and segmenting lymph nodes in a 3D medical image, such as a CT image, is disclosed. A plurality of lymph node center point candidates are detected in the 3D medical image. A lymph node candidate is segmented for each of the detected lymph node center point candidates. Lymph nodes are detected from the segmented lymph node candidates by verifying the segmented lymph node candidates using a trained lymph node classifier.
摘要翻译： 公开了一种用于在诸如CT图像的3D医学图像中自动检测和分割淋巴结的方法和系统。 在3D医学图像中检测到多个淋巴结中心点候选。 为每个检测到的淋巴结中心点候选物分割淋巴结候选物。 通过使用训练有素的淋巴结分类器验证分割的淋巴结候选物，从分割的淋巴结候选物中检测淋巴结。
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6.发明申请Method and System for Automatic Detection and Segmentation of Axillary Lymph Nodes 有权腋窝淋巴结自动检测和分割方法与系统公开(公告)号：US20110222751A1
公开(公告)日：2011-09-15
申请号：US13041569
申请日：2011-03-07
申请人： Adrian Barbu , Michael Suehling , Xun (Jason) Xu , David Liu , Shaohua Kevin Zhou , Dorin Comaniciu
发明人： Adrian Barbu , Michael Suehling , Xun (Jason) Xu , David Liu , Shaohua Kevin Zhou , Dorin Comaniciu
IPC分类号： G06K9/00
CPC分类号： G06T7/11 , G06K2209/051 , G06T7/143 , G06T2207/10081 , G06T2207/30004
摘要： A method and system for automatically detecting and segmenting lymph nodes in a 3D medical image, such as a CT image, is disclosed. A plurality of lymph node center point candidates are detected in the 3D medical image. A lymph node candidate is segmented for each of the detected lymph node center point candidates. Lymph nodes are detected from the segmented lymph node candidates by verifying the segmented lymph node candidates using a trained lymph node classifier.
摘要翻译： 公开了一种用于在诸如CT图像的3D医学图像中自动检测和分割淋巴结的方法和系统。 在3D医学图像中检测到多个淋巴结中心点候选。 为每个检测到的淋巴结中心点候选物分割淋巴结候选物。 通过使用训练有素的淋巴结分类器验证分割的淋巴结候选物，从分割的淋巴结候选物中检测淋巴结。
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7.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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8.发明申请Method and System for Detection 3D Spinal Geometry Using Iterated Marginal Space Learning 有权使用迭代边缘空间学习检测3D脊柱几何的方法和系统公开(公告)号：US20110064291A1
公开(公告)日：2011-03-17
申请号：US12794850
申请日：2010-06-07
IPC分类号： G06K9/00
CPC分类号： G06T7/0087 , G06K2209/055 , G06T7/143 , G06T2207/20076 , G06T2207/20081 , G06T2207/30012
摘要： A method and apparatus for automatic detection and labeling of 3D spinal geometry is disclosed. Cervical, thoracic, and lumbar spine regions are detected in a 3D image. Intervertebral disk candidates are detected in each of the spine regions using iterative marginal space learning (MSL). Using a global probabilistic spine model, a separate one of the intervertebral disk candidates is selected for each of a plurality of labeled intervertebral disk locations.
摘要翻译： 公开了一种用于3D脊柱几何的自动检测和标记的方法和装置。 在3D图像中检测到子宫颈，胸部和腰椎区域。 使用迭代边缘空间学习（MSL）在每个脊柱区域中检测椎间盘候选物。 使用全局概率脊柱模型，为多个标记的椎间盘位置中的每一个选择椎间盘候选物中的单独的一个。
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公开(公告)号：US10551456B2
公开(公告)日：2020-02-04
申请号：US14179860
申请日：2014-02-13
申请人： Joerg Roland , Eva Rothgang , Michael Suehling
发明人： Joerg Roland , Eva Rothgang , Michael Suehling
IPC分类号： G01R33/48 , G06T11/00 , A61B5/00 , A61B90/00 , A61B5/055 , A61B17/00 , G01R33/56 , G01R33/54
摘要： In a method, apparatus and medical imaging system to generate image data based on magnetic resonance (MR) thermometry data, planning data of a region of an examination subject that is to be depicted thermometrically are provided to a processor. Through the processor, segmentation data based on the planning data are generated MR thermometry data are provided to the processor, which generates image data on the basis of the MR thermometry data, using the segmentation data.
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10.发明授权公开(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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