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1.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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2.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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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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4.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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5.发明授权Method and system for automatic detection of spinal bone lesions in 3D medical image data 有权3D医学图像数据中脊髓损伤自动检测方法及系统公开(公告)号：US08693750B2
公开(公告)日：2014-04-08
申请号：US13342248
申请日：2012-01-03
申请人： Michael Wels , Michael Suehling , Shaohua Kevin Zhou , David Liu , Dijia Wu , Christopher V. Alvino , Michael Kelm , Grzegorz Soza , Dorin Comaniciu
发明人： Michael Wels , Michael Suehling , Shaohua Kevin Zhou , David Liu , Dijia Wu , Christopher V. Alvino , Michael Kelm , Grzegorz Soza , Dorin Comaniciu
CPC分类号： G06K9/4614 , G06K9/6256 , G06K2209/055 , G06K2209/40 , G06T7/0012 , G06T2207/10072 , G06T2207/20016 , G06T2207/20081 , G06T2207/30012 , G06T2207/30096
摘要： A method and system for automatic detection and volumetric quantification of bone lesions in 3D medical images, such as 3D computed tomography (CT) volumes, is disclosed. Regions of interest corresponding to bone regions are detected in a 3D medical image. Bone lesions are detected in the regions of interest using a cascade of trained detectors. The cascade of trained detectors automatically detects lesion centers and then estimates lesion size in all three spatial axes. A hierarchical multi-scale approach is used to detect bone lesions using a cascade of detectors on multiple levels of a resolution pyramid of the 3D medical image.
摘要翻译： 公开了一种用于3D医学图像中的骨损伤的自动检测和体积定量的方法和系统，例如3D计算机断层摄影（CT）体积。 在3D医学图像中检测与骨骼区域相对应的兴趣区域。 使用级联的经过训练的检测器在感兴趣的区域中检测到骨损伤。 训练有素的检测器的级联自动检测损伤中心，然后估计所有三个空间轴的病变大小。 使用分级多尺度方法来使用3D医学图像的分辨率金字塔的多个级别上的级联检测器来检测骨损伤。
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6.发明申请Method and System for Automatic Detection of Spinal Bone Lesions in 3D Medical Image Data 有权自动检测3D医学图像数据中脊髓损伤的方法和系统公开(公告)号：US20120183193A1
公开(公告)日：2012-07-19
申请号：US13342248
申请日：2012-01-03
申请人： Michael Wels , Michael Suehling , Shaohua Kevin Zhou , David Liu , Dijia Wu , Christopher V. Alvino , Michael Kelm , Grzegorz Soza , Dorin Comaniciu
发明人： Michael Wels , Michael Suehling , Shaohua Kevin Zhou , David Liu , Dijia Wu , Christopher V. Alvino , Michael Kelm , Grzegorz Soza , Dorin Comaniciu
IPC分类号： G06K9/00
CPC分类号： G06K9/4614 , G06K9/6256 , G06K2209/055 , G06K2209/40 , G06T7/0012 , G06T2207/10072 , G06T2207/20016 , G06T2207/20081 , G06T2207/30012 , G06T2207/30096
摘要： A method and system for automatic detection and volumetric quantification of bone lesions in 3D medical images, such as 3D computed tomography (CT) volumes, is disclosed. Regions of interest corresponding to bone regions are detected in a 3D medical image. Bone lesions are detected in the regions of interest using a cascade of trained detectors. The cascade of trained detectors automatically detects lesion centers and then estimates lesion size in all three spatial axes. A hierarchical multi-scale approach is used to detect bone lesions using a cascade of detectors on multiple levels of a resolution pyramid of the 3D medical image.
摘要翻译： 公开了一种用于3D医学图像中的骨损伤的自动检测和体积定量的方法和系统，例如3D计算机断层摄影（CT）体积。 在3D医学图像中检测与骨骼区域相对应的兴趣区域。 使用级联的经过训练的检测器在感兴趣的区域中检测到骨损伤。 训练有素的检测器的级联自动检测损伤中心，然后估计所有三个空间轴的病变大小。 使用分级多尺度方法来使用3D医学图像的分辨率金字塔的多个级别上的级联检测器来检测骨损伤。
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7.发明授权System and method for using a similarity function to perform appearance matching in image pairs 有权使用相似度函数执行图像对中的外观匹配的系统和方法公开(公告)号：US07831074B2
公开(公告)日：2010-11-09
申请号：US11539989
申请日：2006-10-10
申请人： Shaohua Kevin Zhou , Jie Shao , Jonathan Dowdall , Adrian Barbu , Bogdan Georgescu , Dorin Comaniciu
发明人： Shaohua Kevin Zhou , Jie Shao , Jonathan Dowdall , Adrian Barbu , Bogdan Georgescu , Dorin Comaniciu
CPC分类号： G06K9/6257 , G06K2209/05 , G06T7/33 , G06T2207/10124 , G06T2207/10132 , G06T2207/30048
摘要： The present invention is directed to a method for populating a database with a set of images of an anatomical structure. The database is used to perform appearance matching in image pairs of the anatomical structure. A set of image pairs of anatomical structures is received, where each image pair is annotated with a plurality of location-sensitive regions that identify a particular aspect of the anatomical structure. Weak learners are iteratively selected and an image patch is identified. A boosting process is used to identify a strong classifier based on responses to the weak learners applied to the identified image patch for each image pair. The responses comprise a feature response and a location response associated with the image patch. Positive and negative image pairs are generated. The positive and negative image pairs are used to learn a similarity function. The learned similarity function and iteratively selected weak learners are stored in the database.
摘要翻译： 本发明涉及一种用解剖结构的一组图像填充数据库的方法。 该数据库用于在解剖结构的图像对中执行外观匹配。 接收一组解剖结构的图像对，其中每个图像对用多个识别解剖结构的特定方面的位置敏感区域注释。 迭代选择弱学习者，并识别图像补丁。 基于对应用于每个图像对的所识别的图像补丁的弱学习者的响应，使用增强过程来识别强分类器。 响应包括与图像块相关联的特征响应和位置响应。 产生正负图像对。 正负图像对用于学习相似度函数。 学习的相似度函数和迭代选择的弱学习者存储在数据库中。
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8.发明授权System and method for automatic magnetic resonance volume composition and normalization 有权用于自动磁共振体积组成和归一化的系统和方法公开(公告)号：US09147250B2
公开(公告)日：2015-09-29
申请号：US13616548
申请日：2012-09-14
申请人： David Liu , Shaohua Kevin Zhou , Peter Gall , Dorin Comaniciu , Andre de Oliveira , Berthold Kiefer
发明人： David Liu , Shaohua Kevin Zhou , Peter Gall , Dorin Comaniciu , Andre de Oliveira , Berthold Kiefer
IPC分类号： A61B5/055 , G06T7/00 , G01R33/56 , G01R33/565
CPC分类号： G06T7/0034 , A61B5/055 , G01R33/5608 , G01R33/565 , G06T7/35 , G06T2207/10088 , G06T2207/30004
摘要： A method and system for automatic magnetic resonance (MR) volume composition and normalization is disclosed. In one embodiment, a plurality of MR volumes is received. A composite MR volume is generated from the plurality of MR volumes. Volume normalization of the composite MR volume is then performed to correct intensity inhomogeneity in the composite MR volume. The volume normalization of the composite MR volume may be performed using template MR volume or without a template MR volume.
摘要翻译： 公开了一种用于自动磁共振（MR）体积组成和归一化的方法和系统。 在一个实施例中，接收多个MR卷。 从多个MR卷生成复合MR卷。 然后执行复合MR体积的体积归一化以校正复合MR体积中的强度不均匀性。 可以使用模板MR体积或不具有模板MR体积来执行复合MR体积的体积归一化。
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9.发明授权Method and system for patient-specific computational modeling and simulation for coupled hemodynamic analysis of cerebral vessels 有权用于患者特异性计算建模和模拟脑血管耦合血流动力学分析的方法和系统公开(公告)号：US09141763B2
公开(公告)日：2015-09-22
申请号：US13366677
申请日：2012-02-06
申请人： Puneet Sharma , Tommaso Mansi , Viorel Mihalef , Jingdan Zhang , David Liu , Shaohua Kevin Zhou , Bogdan Georgescu , Dorin Comaniciu
发明人： Puneet Sharma , Tommaso Mansi , Viorel Mihalef , Jingdan Zhang , David Liu , Shaohua Kevin Zhou , Bogdan Georgescu , Dorin Comaniciu
CPC分类号： G06F19/3437 , G06F19/00 , G16H50/50
摘要： A method and system for patient-specific computational modeling and simulation for coupled hemodynamic analysis of cerebral vessels is disclosed. An anatomical model of a cerebral vessel is extracted from 3D medical image data. The anatomical model of the cerebral vessel includes an inner wall and an outer wall of the cerebral vessel. Blood flow in the cerebral vessel and deformation of the cerebral vessel wall are simulated using coupled computational fluid dynamics (CFD) and computational solid mechanics (CSM) simulations based on the anatomical model of the cerebral vessel.
摘要翻译： 公开了一种用于脑血管耦合血流动力学分析的患者特异性计算建模和模拟方法及系统。 从3D医学图像数据中提取脑血管的解剖模型。 脑血管的解剖模型包括脑血管的内壁和外壁。 基于脑血管解剖模型，使用耦合计算流体动力学（CFD）和计算固体力学（CSM）模拟模拟脑血管中的血流和脑血管壁的变形。
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10.发明授权Method and system for up-vector detection for ribs in computed tomography volumes 有权计算机断层扫描体积中肋骨向上检测的方法和系统公开(公告)号：US09020233B2
公开(公告)日：2015-04-28
申请号：US13602660
申请日：2012-09-04
申请人： David Liu , Hao Xu , Dijia Wu , Christian Tietjen , Grzegorz Soza , Shaohua Kevin Zhou , Dorin Comaniciu
发明人： David Liu , Hao Xu , Dijia Wu , Christian Tietjen , Grzegorz Soza , Shaohua Kevin Zhou , Dorin Comaniciu
摘要： A method and system for up-vector detection for ribs in a 3D medical image volume, such as a computed tomography (CT) volume is disclosed. A rib centerline of at least one rib is extracted in a 3D medical image volume. An up-vector is automatically detected at each of a plurality of centerline points of the rib centerline of the at least one rib. The up-vector at each centerline point can be detected using a trained regression function. Alternatively, the up-vector at each centerline point can be detected by detecting an ellipse shape in a cross-sectional rib image generated at each centerline point.
摘要翻译： 公开了一种用于3D医学图像体积中的肋的上矢量检测的方法和系统，例如计算机断层摄影（CT）体积。 在3D医学图像体积中提取至少一个肋的肋中心线。 在至少一个肋的肋中心线的多个中心线点的每一个处自动检测向上矢量。 可以使用经过训练的回归函数检测每个中心线点处的向上矢量。 或者，可以通过在每个中心线点处产生的横截面肋图像中检测椭圆形状来检测每个中心线点处的向上矢量。
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