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1.发明申请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正规化的系统和方法公开(公告)号：US20130072782A1
公开(公告)日：2013-03-21
申请号：US13616548
申请日：2012-09-14
申请人： David Liu , Shaohua Kevin Zhou , Peter Gall , Dorin Comaniciu , Andre de Aliveira , Berthold Kiefer
发明人： David Liu , Shaohua Kevin Zhou , Peter Gall , Dorin Comaniciu , Andre de Aliveira , Berthold Kiefer
IPC分类号： A61B5/055
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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2.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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3.发明授权Method and system for automatic prostate segmentation in magnetic resonance images 有权磁共振图像自动前列腺分割的方法和系统公开(公告)号：US09269156B2
公开(公告)日：2016-02-23
申请号：US13947300
申请日：2013-07-22
申请人： Neil Birkbeck , Jingdan Zhang , Martin Requardt , Berthold Kiefer , Peter Gall , Shaohua Kevin Zhou
发明人： Neil Birkbeck , Jingdan Zhang , Martin Requardt , Berthold Kiefer , Peter Gall , Shaohua Kevin Zhou
CPC分类号： G06T7/0081 , G06T5/009 , G06T7/11 , G06T7/149 , G06T2200/04 , G06T2207/10088 , G06T2207/20016 , G06T2207/20081 , G06T2207/30081
摘要： A method and system for fully automatic segmentation the prostate in magnetic resonance (MR) image data is disclosed. Intensity normalization is performed on an MR image of a patient to adjust for global contrast changes between the MR image and other MR scans and to adjust for intensity variation within the MR image due to an endorectal coil used to acquire the MR image. An initial prostate segmentation in the MR image is obtained by aligning a learned statistical shape model of the prostate to the MR image using marginal space learning (MSL). The initial prostate segmentation is refined using one or more trained boundary classifiers.
摘要翻译： 公开了一种在磁共振（MR）图像数据中全息自动分割前列腺的方法和系统。 对患者的MR图像进行强度归一化，以调整MR图像和其他MR扫描之间的全局对比度变化，并且由于用于获取MR图像的直肠内线圈来调整MR图像内的强度变化。 通过使用边缘空间学习（MSL）将前列腺的学习统计形状模型与MR图像对准，获得MR图像中的初始前列腺分割。 使用一个或多个训练有素的边界分类器来改进初始前列腺分割。
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4.发明申请Method and System for Automatic Prostate Segmentation in Magnetic Resonance Images 有权磁共振图像中自动前列腺分割的方法与系统公开(公告)号：US20140029823A1
公开(公告)日：2014-01-30
申请号：US13947300
申请日：2013-07-22
申请人： Neil Birkbeck , Jingdan Zhang , Martin Requardt , Berthold Kiefer , Peter Gall , Shaohua Kevin Zhou
发明人： Neil Birkbeck , Jingdan Zhang , Martin Requardt , Berthold Kiefer , Peter Gall , Shaohua Kevin Zhou
IPC分类号： G06T7/00
CPC分类号： G06T7/0081 , G06T5/009 , G06T7/11 , G06T7/149 , G06T2200/04 , G06T2207/10088 , G06T2207/20016 , G06T2207/20081 , G06T2207/30081
摘要： A method and system for fully automatic segmentation the prostate in magnetic resonance (MR) image data is disclosed. Intensity normalization is performed on an MR image of a patient to adjust for global contrast changes between the MR image and other MR scans and to adjust for intensity variation within the MR image due to an endorectal coil used to acquire the MR image. An initial prostate segmentation in the MR image is obtained by aligning a learned statistical shape model of the prostate to the MR image using marginal space learning (MSL). The initial prostate segmentation is refined using one or more trained boundary classifiers.
摘要翻译： 公开了一种在磁共振（MR）图像数据中全息自动分割前列腺的方法和系统。 对患者的MR图像进行强度归一化，以调整MR图像和其他MR扫描之间的全局对比度变化，并且由于用于获取MR图像的直肠内线圈来调整MR图像内的强度变化。 通过使用边缘空间学习（MSL）将前列腺的学习统计形状模型与MR图像对准，获得MR图像中的初始前列腺分割。 使用一个或多个训练有素的边界分类器来改进初始前列腺分割。
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5.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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6.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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7.发明申请Method and System for Up-Vector Detection for Ribs in Computed Tomography Volumes 有权计算机断层扫描中肋骨上向量检测的方法和系统公开(公告)号：US20130070996A1
公开(公告)日：2013-03-21
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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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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公开(公告)号：US08761480B2
公开(公告)日：2014-06-24
申请号：US13229953
申请日：2011-09-12
CPC分类号： G06K9/00 , G06K2209/05 , G06T7/0012 , G06T7/73 , G06T2207/10028 , G06T2207/10081 , G06T2207/20044 , G06T2207/20081 , G06T2207/30101 , G06T2207/30172 , G06T2207/30204
摘要： The present invention provides a method and system for vascular landmark detection in CT volumes. A CT volume is received and an initial position of a plurality of vascular landmarks is detected. The initial position of each of the plurality of vascular landmarks is then adjusted in order to position each vascular landmark inside a vessel lumen. A new position of each of the plurality of vascular landmarks representing the adjusted initial positions is output.
摘要翻译： 本发明提供了CT体积中血管标志物检测的方法和系统。 接收CT体积并检测多个血管界标的初始位置。 然后调整多个血管标志中的每一个的初始位置，以便将血管标记位于血管内腔内。 输出表示调整后的初始位置的多个血管界标中的每一个的新位置。
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10.发明授权Method and system for automatic rib centerline extraction using learning based deformable template matching 有权使用基于学习的可变形模板匹配的自动肋骨中心线提取的方法和系统公开(公告)号：US08989471B2
公开(公告)日：2015-03-24
申请号：US13602730
申请日：2012-09-04
申请人： Dijia Wu , David Liu , Christian Tietjen , Grzegorz Soza , Shaohua Kevin Zhou , Dorin Comaniciu
发明人： Dijia Wu , David Liu , Christian Tietjen , Grzegorz Soza , Shaohua Kevin Zhou , Dorin Comaniciu
CPC分类号： G06K9/6207 , G06T7/12 , G06T7/149 , G06T2207/10072 , G06T2207/20081 , G06T2207/20116 , G06T2207/30008 , G06T2207/30172
摘要： A method and system for extracting rib centerlines in a 3D volume, such as a 3D computed tomography (CT) volume, is disclosed. Rib centerline voxels are detected in the 3D volume using a learning based detector. Rib centerlines or the whole rib cage are then extracted by matching a template of rib centerlines for the whole rib cage to the 3D volume based on the detected rib centerline voxels. Each of the extracted rib centerlines are then individually refined using an active contour model.
摘要翻译： 公开了一种用于在3D体积中提取肋骨中心线的方法和系统，例如3D计算机断层摄影（CT）体积。 使用基于学习的检测器在3D体积中检测肋中心线体素。 然后通过将基于检测到的肋骨中心线体素的整个肋骨架的肋骨中心线的模板与3D体积匹配来提取肋中心线或整个肋骨笼。 然后使用主动轮廓模型分别提取每个提取的肋中心线。
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