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51. 发明授权

US08693750B2 Method and system for automatic detection of spinal bone lesions in 3D medical image data 有权
标题翻译： 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
IPC分类号： G06K9/00 , G06K9/46 , G06K9/62 , G06K9/68 , G06K9/70
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。

52. 发明授权

US08605969B2 Method and system for multiple object detection by sequential Monte Carlo and hierarchical detection network 有权
标题翻译：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
申请人： Michal Sofka , Jingdan Zhang , Shaohua Kevin Zhou , Dorin Comaniciu
发明人： Michal Sofka , Jingdan Zhang , Shaohua Kevin Zhou , Dorin Comaniciu
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。

53. 发明申请

US20130077841A1 Method and System for Automatic Rib Centerline Extraction Using Learning Base Deformable Template Matching 有权
标题翻译：使用学习基础变形模板匹配的自动肋中心线提取的方法和系统
公开(公告)号：US20130077841A1
公开(公告)日：2013-03-28
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
IPC分类号： G06K9/46
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。

54. 发明申请

US20130072782A1 SYSTEM AND METHOD FOR AUTOMATIC MAGNETIC RESONANCE VOLUME COMPOSITION AND NORMALIZATION 有权
标题翻译：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。

55. 发明申请

US20120183193A1 Method and System for Automatic Detection of Spinal Bone Lesions in 3D Medical Image Data 有权
标题翻译：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。

56. 发明申请

US20120070053A1 Method and System for Vascular Landmark Detection 有权
标题翻译：血管标志检测方法与系统
公开(公告)号：US20120070053A1
公开(公告)日：2012-03-22
申请号：US13229953
申请日：2011-09-12
申请人： David Liu , Shaohua Kevin Zhou , Dominik Bernhardt , Dorin Comaniciu
发明人： David Liu , Shaohua Kevin Zhou , Dominik Bernhardt , Dorin Comaniciu
IPC分类号： G06K9/00
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。

57. 发明申请

US20100254582A1 System and Method for Detecting Landmarks in a Three-Dimensional Image Volume 审中-公开
标题翻译：用于检测三维图像体积中的地标的系统和方法
公开(公告)号：US20100254582A1
公开(公告)日：2010-10-07
申请号：US12728315
申请日：2010-03-22
申请人： David Liu , Shaohua Kevin Zhou , Sascha Seifert , Dominik Bernhardt , Dorin Comaniciu
发明人： David Liu , Shaohua Kevin Zhou , Sascha Seifert , Dominik Bernhardt , Dorin Comaniciu
IPC分类号： G06K9/00
CPC分类号： G06K9/4638 , G06K2009/00932 , G06K2209/05 , G06T7/0012 , G06T7/46 , G06T2207/10081 , G06T2207/20016 , G06T2207/20072 , G06T2207/20081 , G06T2207/30101
摘要： A method and apparatus for detecting vascular landmarks in a 3D image volume, such as a CT volume, is disclosed. One or more guide slices are detected in a 3D image volume. A set of landmark candidates for multiple target vascular landmarks are then detected based on the guide slices. A node potential value for each landmark candidate is generated based on an error value determined using spatial histogram-based error regression, and edge potential values for pairs of landmark candidates are generated based on a bifurcation analysis of the image volume using vessel tracing. The optimal landmark candidate for each target landmark is then determined using a Markov random field model based on the node potential values and the edge potential values.
摘要翻译：公开了用于检测诸如CT体积的3D图像体积中的血管界标的方法和装置。在3D图像体积中检测到一个或多个引导切片。然后基于引导片检测一组用于多个目标血管标志的地标候选物。基于使用基于空间直方图的误差回归确定的误差值来生成每个地标候选的节点电位值，并且基于使用容器跟踪的图像体积的分岔分析来生成地标候选对的边缘电势值。然后使用基于节点电位值和边缘电位值的马尔科夫随机场模型来确定每个目标地标的最佳地标候选。

58. 发明申请

US20100080434A1 Method and System for Hierarchical Parsing and Semantic Navigation of Full Body Computed Tomography Data 有权
标题翻译：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。

59. 发明申请

US20090310837A1 Method and System for Automatic Detection and Measurement of Mitral Valve Inflow Patterns in Doppler Echocardiography 有权
标题翻译：多普勒超声心动图二尖瓣流入模式自动检测和测量方法与系统
公开(公告)号：US20090310837A1
公开(公告)日：2009-12-17
申请号：US12481861
申请日：2009-06-10
申请人： Jin-hyeong Park , Shaohua Kevin Zhou , John I. Jackson , Dorin Comaniciu
发明人： Jin-hyeong Park , Shaohua Kevin Zhou , John I. Jackson , Dorin Comaniciu
IPC分类号： G06K9/00
CPC分类号： G06K9/0053 , A61B8/0883 , A61B8/488 , G06K2209/05
摘要： A method and system for segmentation of mitral valve inflow (MI) patterns in Doppler echocardiogram images is disclosed. Trained root detectors are used to detect left root candidates, right root candidates, and peak candidates in an input Doppler echocardiogram image. Two global structure detectors, a single triangle detector for non-overlapping E-waves and A-waves and a double triangle detector for overlapping E-waves and A-waves, are used to detect single triangle candidates and double triangle candidates based on the left root, right root, and peak candidates. A shape profile is used to determine a shape probability for each of the single triangle candidates and each of the double triangle candidates. The best single triangle candidate and the best double triangle candidate are selected based on shape probability and detection probability. One of the best single triangle candidate and the best double triangle candidate is selected as the final segmentation result based on a shape probability comparison.
摘要翻译：公开了一种用于多普勒超声心动图图像中二尖瓣流入（MI）模式分割的方法和系统。训练的根检测器用于检测输入多普勒超声心动图图像中的左根候选，右根候选和峰候选。两个全局结构检测器，用于非重叠E波和A波的单个三角形检测器和用于重叠E波和A波的双三角形检测器用于基于左侧检测单个三角形候选和双三角形候选根，右根和峰值候选。形状轮廓用于确定每个单个三角形候选和每个双三角形候选的形状概率。基于形状概率和检测概率选择最佳单三角候选和最佳双三角候选。基于形状概率比较，选择最佳单三角候选和最佳双三角候选之一作为最终分割结果。

60. 发明申请

US20090010509A1 Method and system for detection of deformable structures in medical images 有权
标题翻译：用于检测医学图像中可变形结构的方法和系统
公开(公告)号：US20090010509A1
公开(公告)日：2009-01-08
申请号：US12214339
申请日：2008-06-18
申请人： Shaohua Kevin Zhou , Feng Guo , Jin-Hyeong Park , Gustavo Henrique Monteiro de Barros Carneiro , Constantine Simopoulos , Joanne Otsuki , Dorin Comaniciu , John I. Jackson
发明人： Shaohua Kevin Zhou , Feng Guo , Jin-Hyeong Park , Gustavo Henrique Monteiro de Barros Carneiro , Constantine Simopoulos , Joanne Otsuki , Dorin Comaniciu , John I. Jackson
IPC分类号： G06K9/62
CPC分类号： G06K9/0055 , G06K9/6206 , G06K9/6256
摘要： A method and system for detection of deformable structures in medical images is disclosed. Deformable structures can represent blood flow patterns in images such as Doppler echocardiograms. A probabilistic, hierarchical, and discriminant framework is used to detect such deformable structures. This framework integrates evidence from different primitive levels via a progressive detector hierarchy, including a series of discriminant classifiers. A target deformable structure is parameterized by a multi-dimensional parameter, and primitives or partial parameterizations of the parameter are determined. An input image is received, and a series of primitives are sequentially detected using the progressive detector hierarchy, in which each detector or classifier detects a corresponding primitive. The final detector detects configuration candidates for the deformable structure.
摘要翻译：公开了用于检测医学图像中的可变形结构的方法和系统。可变形结构可以表示图像中的血流模式，例如多普勒超声心动图。概率，分层和判别框架用于检测这种可变形结构。该框架通过渐进式检测器层次结合不同原始级别的证据，包括一系列判别分类器。目标可变形结构由多维参数进行参数化，并确定参数的基元或部分参数化。接收输入图像，并且使用逐行检测器层级顺序地检测一系列图元，其中每个检测器或分类器检测相应的图元。最终检测器检测可变形结构的配置候选。

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