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1. 发明申请

WO2014008332A2 SYSTEM AND METHODS FOR INTEGRATED AND PREDICTIVE ANALYSIS OF MOLECULAR, IMAGING, AND CLINICAL DATA FOR PATIENT-SPECIFIC MANAGEMENT OF DISEASES 审中-公开
标题翻译：用于疾病特异性管理的分子，成像和临床数据的综合和预测分析的系统和方法
公开(公告)号：WO2014008332A2
公开(公告)日：2014-01-09
申请号：PCT/US2013049205
申请日：2013-07-03
申请人： SIEMENS CORP , SIEMENS AG OESTERREICH , SIEMENS AG , MANSI TOMMASO , LIM WEI KEAT , KING VANESSA , KREMER ANDREAS , GEORGESCU BOGDAN , ZHENG XUDONG , KAMEN ALI , KELLER ANDREAS , STAEHLER CORD FRIEDRICH , WIRSZ EMIL , COMANICIU DORIN
发明人： MANSI TOMMASO , LIM WEI KEAT , KING VANESSA , KREMER ANDREAS , GEORGESCU BOGDAN , ZHENG XUDONG , KAMEN ALI , KELLER ANDREAS , STAEHLER CORD FRIEDRICH , WIRSZ EMIL , COMANICIU DORIN
IPC分类号： G06F19/00
CPC分类号： G06F19/3437 , G06F19/12 , G16H50/50
摘要： A system operating in a plurality of modes to provide an integrated analysis of molecular data, imaging data, and clinical data associated with a patient includes a multi-scale model, a molecular model, and a linking component. The multi-scale model is configured to generate one or more estimated multi-scale parameters based on the clinical data and the imaging data when the system operates in a first mode, and generate a model of organ functionality based on one or more inferred multi-scale parameters when the system operates in a second mode. The molecular model is configured to generate one or more first molecular findings based on a molecular network analysis of the molecular data, wherein the molecular model is constrained by the estimated parameters when the system operates in the first mode. The linking component, which is operably coupled to the multi-scale model and the molecular model, is configured to transfer the estimated multi-scale parameters from the multi-scale model to the molecular model when the system operates in the first mode, and generate, using a machine learning process, the inferred multi-scale parameters based on the molecular findings when the system operates in the second mode.
摘要翻译：以多种模式操作以提供与患者相关联的分子数据，成像数据和临床数据的综合分析的系统包括多尺度模型，分子模型和连接部件。多尺度模型被配置为当系统以第一模式操作时，基于临床数据和成像数据生成一个或多个估计的多尺度参数，并且基于一个或多个推断的多尺度模型生成器官功能模型，系统在第二模式下运行时的缩放参数。分子模型被配置为基于分子数据的分子网络分析产生一个或多个第一分子发现，其中当系统以第一模式操作时，分子模型受到估计参数约束。可操作地耦合到多尺度模型和分子模型的连接部件被配置为当系统在第一模式下操作时将估计的多尺度参数从多尺度模型传递到分子模型，并且生成，使用机器学习过程，当系统在第二模式下操作时，基于分子发现的推断的多尺度参数。

2. 发明申请

WO2013028762A1 METHOD AND SYSTEM FOR INTEGRATED RADIOLOGICAL AND PATHOLOGICAL INFORMATION FOR DIAGNOSIS, THERAPY SELECTION, AND MONITORING 审中-公开
标题翻译：用于诊断，治疗选择和监测的综合放射学和病理学信息的方法和系统
公开(公告)号：WO2013028762A1
公开(公告)日：2013-02-28
申请号：PCT/US2012/051869
申请日：2012-08-22
申请人： SIEMENS CORPORATION , KAMEN, Ali , GRADY, Leo , PALADINI, Gianluca , KHURD, Parmeshwar , KUTTER, Oliver , COMANICIU, Dorin
发明人： KAMEN, Ali , GRADY, Leo , PALADINI, Gianluca , KHURD, Parmeshwar , KUTTER, Oliver , COMANICIU, Dorin
IPC分类号： A61B6/00 , A61B8/00 , G06F19/00 , A61B10/00
CPC分类号： G06T7/0012 , A61B6/12 , A61B6/463 , A61B6/467 , A61B8/0841 , A61B8/463 , A61B8/465 , A61B8/467 , A61B10/02 , G06F19/00 , G06F19/3481 , G06T2207/30004 , G16H50/20
摘要： A method and system for integrating radiological and pathological information for cancer diagnosis, therapy selection, and monitoring is disclosed. A radiological image of a patient, such as a magnetic resonance (MR), computed tomography (CT), positron emission tomography (PET), or ultrasound image, is received. A location corresponding to each of one or more biopsy samples is determined in the at least one radiological image. An integrated display is used to display a histological image corresponding to the each biopsy samples, the radiological image, and the location corresponding to each biopsy samples in the radiological image. Pathological information and radiological information are integrated by combining features extracted from the histological images and the features extracted from the corresponding locations in the radiological image for cancer grading, prognosis prediction, and therapy selection.
摘要翻译：公开了用于整合放射学和病理学信息用于癌症诊断，治疗选择和监测的方法和系统。接收患者的放射学图像，例如磁共振（MR），计算机断层摄影（CT），正电子发射断层摄影（PET）或超声图像。在至少一个放射图像中确定与一个或多个活检样本中的每一个对应的位置。使用集成显示器来显示对应于每个活检样本，放射图像和对应于放射图像中的每个活检样本的位置的组织学图像。通过组合图像提取的特征和从放射图像中相应位置提取的特征，进行癌症分级，预后预测和治疗选择，整合病理信息和放射学信息。

3. 发明申请

WO2012064917A1 ROBOTIC NAVIGATED NUCLEAR PROBE IMAGING 审中-公开
标题翻译：机动航海导弹探测成像
公开(公告)号：WO2012064917A1
公开(公告)日：2012-05-18
申请号：PCT/US2011/060091
申请日：2011-11-10
申请人： SIEMENS CORPORATION , PALADINI, Gianluca , KAMEN, Ali , KUTTER, Oliver
发明人： PALADINI, Gianluca , KAMEN, Ali , KUTTER, Oliver
IPC分类号： A61B6/00 , A61B5/00 , A61B17/34 , G01T1/166 , G01T1/29 , A61B19/00
CPC分类号： A61B6/4458 , A61B5/0064 , A61B6/037 , A61B6/4057 , A61B6/4258 , A61B6/4417 , A61B6/4464 , A61B6/466 , A61B6/481 , A61B6/5223 , A61B6/5229 , A61B6/5247 , A61B34/30
摘要： Robotic navigation is provided for nuclear probe imaging. Using a three-dimensional scanner (19), the surface of a patient is determined (42). A calibrated robotic system positions (48) a nuclear probe about the patient based on the surface. The positioning (48) may be without contacting the patient and the surface may be used in reconstruction to account for spacing of the probe from the patient. By using the robotic system for positioning (48), the speed, resolution and/or quality of the reconstructed image may be predetermined, user settable, and/or improved compared to manual scanning. The reconstruction (52) may be more computationally efficient by providing for regular spacing of radiation detection locations within the volume
摘要翻译：提供机器人导航用于核探针成像。使用三维扫描器（19），确定患者的表面（42）。校准的机器人系统基于表面定位（48）关于患者的核探针。定位（48）可以不与患者接触，并且可以在重建中使用表面以考虑探针与患者的间隔。通过使用用于定位的机器人系统（48），与手动扫描相比，重建图像的速度，分辨率和/或质量可以是预定的，用户可设置的和/或改进的。通过提供体积内的辐射检测位置的规则间隔，重建（52）可以更有计算效率

4. 发明申请

WO2011109710A1 HIERARCHICAL ATLAS-BASED SEGMENTATION 审中-公开
标题翻译：基于分层的基于ATLAS的分类
公开(公告)号：WO2011109710A1
公开(公告)日：2011-09-09
申请号：PCT/US2011/027189
申请日：2011-03-04
申请人： SIEMENS CORPORATION , SIEMENS AKTIENGESELLSCHAFT , SUNDAR, Hari , KAMEN, Ali , THOMAS, Boettger
发明人： SUNDAR, Hari , KAMEN, Ali , THOMAS, Boettger
IPC分类号： G06T7/00
CPC分类号： G06T7/0024 , G06T7/11 , G06T7/149 , G06T7/30 , G06T2207/10081 , G06T2207/20016 , G06T2207/20128 , G06T2207/30016
摘要： A method for segmenting an image includes registering an annotated template image to an acquired reference image using only rigid transformations to define a transformation function relating the annotated template image to the acquired reference image (S101). The defined transformation function is refined by registering the annotated template image to the acquired reference image using only affine transformations (S102). The refined transformation function is further refined by registering the annotated template image to the acquired reference image using only multi-affine transformations (S103). The twice refined transformation function is further refined by registering the annotated template image to the acquired reference image using deformation transformations (S104).
摘要翻译：用于分割图像的方法包括使用刚性变换将注释的模板图像注册到所获取的参考图像，以定义将所注释的模板图像与所获取的参考图像相关联的变换功能（S101）。通过使用仿射变换将注释的模板图像注册到所获取的参考图像来改进所定义的变换函数（S102）。通过仅使用多仿射变换将注释的模板图像注册到所获取的参考图像，进一步改进了精细变换功能（S103）。通过使用变形变换将所注释的模板图像注册到获取的参考图像来进一步改进两次精化转换函数（S104）。

5. 发明申请

WO2014078615A1 METHOD TO IDENTIFY OPTIMUM CORONARY ARTERY DISEASE TREATMENT 审中-公开
标题翻译：识别最佳冠状动脉疾病治疗的方法
公开(公告)号：WO2014078615A1
公开(公告)日：2014-05-22
申请号：PCT/US2013/070225
申请日：2013-11-15
申请人： SIEMENS HEALTHCARE DIAGNOSTICS INC. , KAMEN, Ali , SINGH, Maneesh Kumar , POELSTERL, Sebastian , LADIC, Lance Anthony , COMANICIU, Dorin
发明人： KAMEN, Ali , SINGH, Maneesh Kumar , POELSTERL, Sebastian , LADIC, Lance Anthony , COMANICIU, Dorin
IPC分类号： G06F19/00
CPC分类号： G06F19/345 , G06F19/00 , G06N3/0472 , G06N3/08 , G16H50/20
摘要： A method of identifying an optimum treatment for a patient suffering from coronary artery disease, comprising: (i) providing patient information selected from: (a) status in the patient of one or more coronary disease associated biomarkers; (b) one or more items of medical history information selected from prior condition history, intervention history and medication history; (c) one or more items of diagnostic history, if the patient has a diagnostic history; and (d) one or more items of demographic data; (ii) aggregating the patient information in: (a) a Bayesian network; (b) a machine learning and neural network; (c) a rule-based system; and (d) a regression-based system; (iii) deriving a predicted probabilistic adverse event outcome for each intervention comprising percutaneous coronary intervention by placement of a bare metal stent, or a drug-coated stent; or by coronary artery bypass grafting; and (iv) determining the intervention having the lowest predicted probabilistic adverse outcome.
摘要翻译：一种鉴定患有冠状动脉疾病的患者的最佳治疗的方法，包括：（i）提供患者信息，所述患者信息选自：（a）患者中一种或多种冠状动脉疾病相关生物标志物的状态; （b）从先前情况史，干预史和用药史选择的一项或多项病史信息; （c）患者具有诊断史的一项或多项诊断史; 和（d）一个或多个人口统计数据项目; （ii）在以下方面聚合患者信息：（a）贝叶斯网络; （b）机器学习和神经网络; （c）基于规则的制度; 和（d）基于回归的系统; （iii）通过放置裸金属支架或药物涂层支架，导出包括经皮冠状动脉介入的每个干预的预测概率不良事件结果; 或通过冠状动脉旁路移植术; 和（iv）确定具有最低预测概率不良结果的干预。

6. 发明申请

WO2013071219A1 METHOD AND SYSTEM FOR MULTI-SCALE ANATOMICAL AND FUNCTIONAL MODELING OF CORONARY CIRCULATION 审中-公开
标题翻译：冠状循环的多尺度解剖学和功能建模方法与系统
公开(公告)号：WO2013071219A1
公开(公告)日：2013-05-16
申请号：PCT/US2012/064604
申请日：2012-11-12
申请人： SIEMENS CORPORATION , SHARMA, Puneet , ITU, Lucian Mihai , GEORGESCU, Bogdan , MIHALEF, Viorel , KAMEN, Ali , COMANICIU, Dorin
发明人： SHARMA, Puneet , ITU, Lucian Mihai , GEORGESCU, Bogdan , MIHALEF, Viorel , KAMEN, Ali , COMANICIU, Dorin
IPC分类号： G06F19/00
CPC分类号： G06F19/12 , G06F19/00 , G06F19/321 , G16H50/30 , G16H50/50
摘要： A method and system for multi-scale anatomical and functional modeling of coronary circulation is disclosed. A patient-specific anatomical model of coronary arteries and the heart is generated from medical image data of a patient. A multi-scale functional model of coronary circulation is generated based on the patient-specific anatomical model. Blood flow is simulated in at least one stenosis region of at least one coronary artery using the multi-scale function model of coronary circulation. Hemodynamic quantities, such as fractional flow reserve (FFR), are computed to determine a functional assessment of the stenosis, and virtual intervention simulations are performed using the multi-scale function model of coronary circulation for decision support and intervention planning.
摘要翻译：公开了一种用于冠状循环多尺度解剖和功能建模的方法和系统。冠状动脉和心脏的患者特异性解剖模型由患者的医学图像数据产生。基于患者特异性解剖模型产生冠状动脉循环的多尺度功能模型。使用冠状动脉循环的多尺度功能模型在至少一个冠状动脉的至少一个狭窄区域中模拟血流量。计算血流动力学量，例如分数流量储备（FFR），以确定狭窄的功能评估，并使用冠状动脉循环的多尺度函数模型进行虚拟干预模拟，以进行决策支持和干预计划。

7. 发明申请

WO2013022688A1 AUTOMATED DETECTION OF DIAGNOSTICALLY RELEVANT REGIONS IN PATHOLOGY IMAGES 审中-公开
标题翻译：自动检测病理学图像中的诊断相关区域
公开(公告)号：WO2013022688A1
公开(公告)日：2013-02-14
申请号：PCT/US2012/049275
申请日：2012-08-02
申请人： SIEMENS HEALTHCARE DIAGNOSTICS INC. , BAHLMANN, Claus , PATEL, Amar, H. , JOHNSON, Jeffrey, P. , NI, Jie , CHEKKOURY-IDRISSI, Andrei-chakib , KHURD, Parmeshwar , KAMEN, Ali , GRADY, Leo
发明人： BAHLMANN, Claus , PATEL, Amar, H. , JOHNSON, Jeffrey, P. , NI, Jie , CHEKKOURY-IDRISSI, Andrei-chakib , KHURD, Parmeshwar , KAMEN, Ali , GRADY, Leo
IPC分类号： G06K9/00
CPC分类号： G06T7/0012 , G06K9/0014 , G06K9/4642 , G06K9/6232 , G06K2009/4695 , G06T7/90 , G06T2207/10056 , G06T2207/30024
摘要： A method for distinguishing between different tissue types imaged in a virtual slide includes receiving an image of a tissue (200), wherein the tissue has been treated with a first stain and a second stain, dividing the image into a plurality of image patches (201), accentuating a difference between portions of the tissue stained by the first stain and portions of the tissue stained by the second stain to generated a plurality of preprocessed image patches (202), extracting a plurality of feature descriptors from each of the preprocessed image patches (203) according to a distribution of the portions of the tissue stained by the first stain and the portions of the tissue stained by the second stain, and classifying each of the image patches according to respective the feature descriptors (204).
摘要翻译：用于区分在虚拟幻灯片中成像的不同组织类型的方法包括接收组织的图像（200），其中已经用第一染色和第二染色处理了组织，将图像分成多个图像贴片（201 ），突出由第一染色染色的组织的部分和由第二染色染色的组织的部分之间的差异，以产生多个预处理图像贴片（202），从每个预处理图像片断提取多个特征描述符（203），根据由第一染色染色的组织的部分的分布和由第二染色染色的组织的部分，并根据各个特征描述符（204）对每个图像斑点进行分类。

8. 发明申请

WO2013019856A1 AUTOMATED MALIGNANCY DETECTION IN BREAST HISTOPATHOLOGICAL IMAGES 审中-公开
标题翻译：乳腺组织病理学自动检测恶性肿瘤
公开(公告)号：WO2013019856A1
公开(公告)日：2013-02-07
申请号：PCT/US2012/049155
申请日：2012-08-01
申请人： SIEMENS HEALTHCARE DIAGNOSTICS INC. , CHEKKOURY-IDRISSI, Andrei-chakib , KHURD, Parmeshwar , JOHNSON, Jeffrey, P. , BAHLMANN, Claus , PATEL, Amar, H. , NI, Jie , KAMEN, Ali , GRADY, Leo
发明人： CHEKKOURY-IDRISSI, Andrei-chakib , KHURD, Parmeshwar , JOHNSON, Jeffrey, P. , BAHLMANN, Claus , PATEL, Amar, H. , NI, Jie , KAMEN, Ali , GRADY, Leo
IPC分类号： G06F15/18
CPC分类号： G06K9/00147 , G06K9/6269 , G06K9/629
摘要： A method for automatically classifying tissue includes obtaining training data including a plurality of microscope images that have been manually classified. A plurality of features is calculated from the training data, each of which is a texture feature, a network feature, or a morphometric feature. A subset of features is selected from the calculated subset of features based on both maximum relevance and minimum redundancy. A classifier is trained based on the selected subset of features and the manual classifications. A diagnostic microscope image is classified in a computer-aided diagnostic system using the trained classifier.
摘要翻译：用于自动分类组织的方法包括获得包括手动分类的多个显微镜图像的训练数据。从训练数据计算出多个特征，每个训练数据是纹理特征，网络特征或形态测量特征。基于最大相关性和最小冗余度，从所计算的特征子集中选择特征的子集。基于所选择的特征子集和手动分类来对分类器进行训练。诊断显微镜图像被分类在使用训练有素的分类器的计算机辅助诊断系统中。

9. 发明申请

WO2010132349A1 SYSTEM AND METHOD FOR IMAGE GUIDED PROSTATE CANCER NEEDLE BIOPSY 审中-公开
标题翻译：用于图像引导的癌前病变生物系统和方法
公开(公告)号：WO2010132349A1
公开(公告)日：2010-11-18
申请号：PCT/US2010/034214
申请日：2010-05-10
申请人： SIEMENS CORPORATION , SIEMENS AKTIENGESELLSCHAFT , KAMEN, Ali , WEIN, Wolfgang , KHURD, Parmeshwar , DIALLO, Mamadou , NANKE, Ralf , FEHRE, Jens , KIEFER, Berthold , REQUARDT, Martin , WEISS, Clifford
发明人： KAMEN, Ali , WEIN, Wolfgang , KHURD, Parmeshwar , DIALLO, Mamadou , NANKE, Ralf , FEHRE, Jens , KIEFER, Berthold , REQUARDT, Martin , WEISS, Clifford
IPC分类号： A61B10/02 , A61B19/00
CPC分类号： A61B10/0241 , A61B8/4254 , A61B34/20 , A61B2017/00274 , A61B2017/00694 , A61B2017/3413 , A61B2018/00547 , A61B2034/105 , A61B2034/107 , A61B2034/2051 , A61B2090/367 , A61B2090/374 , A61B2090/378
摘要： In a method for image guided prostate cancer needle biopsy, a first registration is performed to match a first image of a prostate to a second image of the prostate (210). Third images of the prostate are acquired and compounded into a three-dimensional (3D) image (220). The prostate in the compounded 3D image is segmented to show its border (230). A second registration and then a third registration different from the second registration is performed on distance maps generated from the prostate borders of the first image and the compounded 3D image, wherein the first and second registrations are based on a biomechanical property of the prostate (240). A region of interest in the first image is mapped to the compounded 3D image or a fourth image of the prostate acquired with the second modality (250).
摘要翻译：在用于图像引导的前列腺癌针活检的方法中，执行首先配准以将前列腺的第一图像与前列腺的第二图像（210）匹配。获取前列腺的第三图像并将其复合成三维（3D）图像（220）。复合3D图像中的前列腺被分割以显示其边界（230）。在从第一图像和复合3D图像的前列腺边界生成的距离图上执行与第二登记不同的第二登记和第三登记，其中第一和第二登记基于前列腺的生物力学性质（240 ）。将第一图像中的感兴趣区域映射到复合3D图像或用第二模态获取的前列腺的第四图像（250）。

10. 发明申请

WO2014008332A3 SYSTEM AND METHODS FOR INTEGRATED AND PREDICTIVE ANALYSIS OF MOLECULAR, IMAGING, AND CLINICAL DATA FOR PATIENT-SPECIFIC MANAGEMENT OF DISEASES 审中-公开
公开(公告)号：WO2014008332A3
公开(公告)日：2014-01-09
申请号：PCT/US2013/049205
申请日：2013-07-03
申请人： SIEMENS CORPORATION , SIEMENS AG OSTERREICH , SIEMENS AKTIENGESELLSCHAFT , MANSI, Tommaso , LIM, Wei, Keat , KING, Vanessa , KREMER, Andreas , GEORGESCU, Bogdan , ZHENG, Xudong , KAMEN, Ali , KELLER, Andreas , STAEHLER, Cord, Friedrich , WIRSZ, Emil , COMANICIU, Dorin
发明人： MANSI, Tommaso , LIM, Wei, Keat , KING, Vanessa , KREMER, Andreas , GEORGESCU, Bogdan , ZHENG, Xudong , KAMEN, Ali , KELLER, Andreas , STAEHLER, Cord, Friedrich , WIRSZ, Emil , COMANICIU, Dorin
IPC分类号： G06F19/12 , G06F19/00
摘要： A system operating in a plurality of modes to provide an integrated analysis of molecular data, imaging data, and clinical data associated with a patient includes a multi-scale model, a molecular model, and a linking component. The multi-scale model is configured to generate one or more estimated multi-scale parameters based on the clinical data and the imaging data when the system operates in a first mode, and generate a model of organ functionality based on one or more inferred multi-scale parameters when the system operates in a second mode. The molecular model is configured to generate one or more first molecular findings based on a molecular network analysis of the molecular data, wherein the molecular model is constrained by the estimated parameters when the system operates in the first mode. The linking component, which is operably coupled to the multi-scale model and the molecular model, is configured to transfer the estimated multi-scale parameters from the multi-scale model to the molecular model when the system operates in the first mode, and generate, using a machine learning process, the inferred multi-scale parameters based on the molecular findings when the system operates in the second mode.

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