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21.发明授权Method and system for multiple object detection by sequential Monte Carlo and hierarchical detection network 有权通过连续蒙特卡洛和分层检测网络进行多目标检测的方法和系统公开(公告)号：US08605969B2
公开(公告)日：2013-12-10
申请号：US13080964
申请日：2011-04-06
IPC分类号： G06K9/00
CPC分类号： G06K9/6297 , A61B8/0816 , G06K9/6278 , G06K2209/051 , G06T7/70 , G06T2207/10132 , G06T2207/20016 , G06T2207/20076 , G06T2207/30016 , G06T2207/30048 , G06T2207/30056
摘要： A method and system for detecting multiple objects in an image is disclosed. A plurality of objects in an image is sequentially detected in an order specified by a trained hierarchical detection network. In the training of the hierarchical detection network, the order for object detection is automatically determined. The detection of each object in the image is performed by obtaining a plurality of sample poses for the object from a proposal distribution, weighting each of the plurality of sample poses based on an importance ratio, and estimating a posterior distribution for the object based on the weighted sample poses.
摘要翻译： 公开了一种用于检测图像中的多个对象的方法和系统。 图像中的多个对象以训练有序的分层检测网络指定的顺序被顺序地检测。 在分层检测网络的训练中，自动确定对象检测的顺序。 通过从提案分布中获得用于对象的多个样本姿势，基于重要性比例对多个样本姿势进行加权，并基于该对象的后验分布来估计图像中的每个对象的检测 加权样本姿势。
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22.发明申请Method and System for Multi-Organ Segmentation Using Learning-Based Segmentation and Level Set Optimization 有权使用基于学习的分割和级别集优化的多器官分割的方法和系统公开(公告)号：US20120230572A1
公开(公告)日：2012-09-13
申请号：US13416508
申请日：2012-03-09
申请人： Timo Kohlberger , Michal Sofka , Jens Wetzl , Jingdan Zhang , Shaohua Kevin Zhou , Neil Birkbeck , Jerome Declerck , Jens Kaftan
发明人： Timo Kohlberger , Michal Sofka , Jens Wetzl , Jingdan Zhang , Shaohua Kevin Zhou , Neil Birkbeck , Jerome Declerck , Jens Kaftan
IPC分类号： G06K9/34
CPC分类号： G06T7/0083 , G06K9/6209 , G06T7/12 , G06T2207/10081 , G06T2207/20081 , G06T2207/20161 , G06T2207/30048 , G06T2207/30084
摘要： A method and system for automatic multi-organ segmentation in a 3D image, such as a 3D computed tomography (CT) volume using learning-base segmentation and level set optimization is disclosed. A plurality of meshes are segmented in a 3D medical image, each mesh corresponding to one of a plurality of organs. A level set in initialized by converting each of the plurality of meshes to a respective signed distance map. The level set optimized by refining the signed distance map corresponding to each one of the plurality of organs to minimize an energy function.
摘要翻译： 公开了一种3D图像自动多器官分割的方法和系统，例如使用学习型基础分割和级别集优化的3D计算机断层摄影（CT）体积。 在3D医学图像中分割多个网格，每个网格对应于多个器官中的一个。 通过将多个网格中的每一个转换为相应的带符号距离图来初始化的级别。 通过精化对应于多个器官中的每一个的有符号距离图来最优化能量函数来优化的级别集合。
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23.发明授权Fast detection of left ventricle and its configuration in 2D/3D echocardiogram using probabilistic boosting network 有权使用概率增强网络快速检测左心室及其在2D / 3D超声心动图中的配置公开(公告)号：US08000497B2
公开(公告)日：2011-08-16
申请号：US11872972
申请日：2007-10-16
CPC分类号： G06K9/6256 , G06K9/00201 , G06K9/3233 , G06K2209/05 , G06T7/73 , G06T2207/30048
摘要： A method for detecting an object of interest in an input image includes the computer-implemented steps of: receiving an image, providing a multi-class pose classifier that identifies a plurality of pose features for estimating a pose of the object of interest, providing a plurality of cascades of serially-linked binary object feature classifiers, each cascade corresponding to different poses of the object of interest in the input image, selecting at least one of the cascades using the estimated pose, and employing the selected cascades to detect instances of the object of interest in the image.
摘要翻译： 一种用于在输入图像中检测感兴趣对象的方法包括以下计算机实现的步骤：接收图像，提供识别用于估计感兴趣对象的姿态的多个姿势特征的多类姿势分类器，提供 多个级联的串联二进制对象特征分类器，每个级联对应于输入图像中感兴趣对象的不同姿态，使用估计姿态选择级联中的至少一个，并且采用所选择的级联来检测 图像中感兴趣的对象。
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24.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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25.发明授权Method and system for automatic lung segmentation in magnetic resonance imaging videos 有权磁共振成像视频中自动肺分割的方法和系统公开(公告)号：US08879810B2
公开(公告)日：2014-11-04
申请号：US13678916
申请日：2012-11-16
CPC分类号： G06T7/0014 , G06T7/12 , G06T7/143 , G06T7/149 , G06T2207/10076 , G06T2207/20076 , G06T2207/20081 , G06T2207/20124 , G06T2207/30061
摘要： A method and system for automatic lung segmentation in magnetic resonance imaging (MRI) images and videos is disclosed. A plurality of predetermined key landmarks of a lung are detected in an MRI image. The key landmarks may be detected using discriminative joint contexts representing combinations of multiple key landmarks. A lung boundary is segmented in the MRI image based on the detected key landmarks. The landmark detection and the lung boundary segmentation can be repeated in each frame of an MRI video.
摘要翻译： 公开了磁共振成像（MRI）图像和视频中自动肺分割的方法和系统。 在MRI图像中检测肺的多个预定键标记。 可以使用表示多个关键地标的组合的区分联合上下文来检测关键地标。 基于检测到的关键地标，在MRI图像中分割肺边界。 可以在MRI视频的每个帧中重复地标检测和肺边界分割。
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26.发明授权Method and system for joint multi-organ segmentation in medical image data using local and global context 有权使用本地和全球语境的医学图像数据联合多器官分割的方法和系统公开(公告)号：US08837771B2
公开(公告)日：2014-09-16
申请号：US13777024
申请日：2013-02-26
申请人： Nathan Lay , Neil Birkbeck , Jingdan Zhang , Jens Guehring , Shaohua Kevin Zhou
发明人： Nathan Lay , Neil Birkbeck , Jingdan Zhang , Jens Guehring , Shaohua Kevin Zhou
CPC分类号： G06K9/34 , G06T7/11 , G06T2207/10088 , G06T2207/20076 , G06T2207/20081 , G06T2207/20124 , G06T2207/20164 , G06T2207/30056 , G06T2207/30084
摘要： A method and system for segmenting multiple organs in medical image data is disclosed. A plurality of landmarks of a plurality of organs are detected in a medical image using an integrated local and global context detector. A global posterior integrates evidence of a plurality of image patches to generate location predictions for the landmarks. For each landmark, a trained discriminative classifier for that landmark evaluates the location predictions for that landmark based on local context. A segmentation of each of the plurality of organs is then generated based on the detected landmarks.
摘要翻译： 公开了用于分割医学图像数据中的多个器官的方法和系统。 使用集成的局部和全局上下文检测器在医学图像中检测多个器官的多个界标。 全局后验集成了多个图像补丁的证据，以生成地标的位置预测。 对于每个地标，一个训练有素的区分性分类器根据本地语境来评估该地标的位置预测。 然后基于检测到的界标生成多个器官中的每一个的分割。
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27.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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公开(公告)号：US20110243407A1
公开(公告)日：2011-10-06
申请号：US13080891
申请日：2011-04-06
申请人： Michal Sofka , Kristof Ralovich , Jingdan Zhang , Shaohua Kevin Zhou , Gianluca Paladini , Dorin Comaniciu
发明人： Michal Sofka , Kristof Ralovich , Jingdan Zhang , Shaohua Kevin Zhou , Gianluca Paladini , Dorin Comaniciu
CPC分类号： G06K9/6256 , G06F19/00 , G06F19/321 , G06T7/0012 , G06T2200/16 , G06T2207/20016
摘要： For cloud-based computer assisted detection, hierarchal detection is used, allowing detection on data at progressively greater resolutions. Detected locations at coarser resolutions are used to limit the data transmitted at greater resolutions. Data is only transmitted for neighborhoods around the previously detected locations. Subsequent detection using higher resolution data refines the locations, but only for regions associated with previous detection. By limiting the number and/or size of regions provided at greater resolutions based on the previous detection, the progressive transmission avoids transmission of some data. Additionally, or alternatively, lossy compression may be used without or with minimal reduction in detection sensitivity.
摘要翻译： 对于基于云的计算机辅助检测，使用层次检测，允许以逐渐更大的分辨率检测数据。 较粗分辨率下的检测位置用于限制以更高分辨率传输的数据。 仅针对先前检测到的位置周围的邻域发送数据。 使用较高分辨率数据的后续检测会优化位置，但仅适用于与先前检测相关的区域。 通过基于先前的检测限制以更大分辨率提供的区域的数量和/或尺寸，逐行传输避免了一些数据的传输。 另外或者可选地，可以使用有损压缩，而不用或以最小的检测灵敏度降低。
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29.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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30.发明授权Method and system for multi-organ segmentation using learning-based segmentation and level set optimization 有权使用基于学习的分割和水平集优化的多器官分割的方法和系统公开(公告)号：US09042620B2
公开(公告)日：2015-05-26
申请号：US13416508
申请日：2012-03-09
申请人： Timo Kohlberger , Michal Sofka , Jens Wetzl , Jingdan Zhang , Shaohua Kevin Zhou , Neil Birkbeck , Jerome Declerck , Jens Kaftan
发明人： Timo Kohlberger , Michal Sofka , Jens Wetzl , Jingdan Zhang , Shaohua Kevin Zhou , Neil Birkbeck , Jerome Declerck , Jens Kaftan
CPC分类号： G06T7/0083 , G06K9/6209 , G06T7/12 , G06T2207/10081 , G06T2207/20081 , G06T2207/20161 , G06T2207/30048 , G06T2207/30084
摘要： A method and system for automatic multi-organ segmentation in a 3D image, such as a 3D computed tomography (CT) volume using learning-base segmentation and level set optimization is disclosed. A plurality of meshes are segmented in a 3D medical image, each mesh corresponding to one of a plurality of organs. A level set in initialized by converting each of the plurality of meshes to a respective signed distance map. The level set optimized by refining the signed distance map corresponding to each one of the plurality of organs to minimize an energy function.
摘要翻译： 公开了一种3D图像自动多器官分割的方法和系统，例如使用学习型基础分割和级别集优化的3D计算机断层摄影（CT）体积。 在3D医学图像中分割多个网格，每个网格对应于多个器官中的一个。 通过将多个网格中的每一个转换为相应的带符号距离图来初始化的级别。 通过精化对应于多个器官中的每一个的有符号距离图来最优化能量函数来优化的级别集合。
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