公开(公告)号：US08812431B2

公开(公告)日：2014-08-19

申请号：US13016259

申请日：2011-01-28

申请人： Ingmar Voigt ,  Dime Vitanovski ,  Razvan Ioan Ionasec ,  Alexey Tsymbal ,  Bogdan Georgescu ,  Shaohua Kevin Zhou ,  Martin Huber ,  Dorin Comaniciu

发明人： Ingmar Voigt ,  Dime Vitanovski ,  Razvan Ioan Ionasec ,  Alexey Tsymbal ,  Bogdan Georgescu ,  Shaohua Kevin Zhou ,  Martin Huber ,  Dorin Comaniciu

IPC分类号： G06F17/30 ,  G06F19/12 ,  G06F19/26 ,  A61B5/05 ,  G06N5/00 ,  G06G7/60

CPC分类号： G06G7/60 ,  G06N5/00

摘要： A method and system for providing medical decision support based on virtual organ models and learning based discriminative distance functions is disclosed. A patient-specific virtual organ model is generated from medical image data of a patient. One or more similar organ models to the patient-specific organ model are retrieved from a plurality of previously stored virtual organ models using a learned discriminative distance function. The patient-specific valve model can be classified into a first class or a second class based on the previously stored organ models determined to be similar to the patient-specific organ model.

摘要翻译： 公开了一种基于虚拟器官模型和基于学习的辨别距离函数提供医学决策支持的方法和系统。 从患者的医学图像数据生成患者特异性虚拟器官模型。 使用学习的鉴别距离函数从多个先前存储的虚拟器官模型检索与患者特异器官模型的一个或多个相似的器官模型。 基于先前存储的被确定为类似于患者特异器官模型的器官模型，患者特异性瓣膜模型可以分为第一类或第二类。

公开(公告)号：US20110191283A1

公开(公告)日：2011-08-04

申请号：US13016259

申请日：2011-01-28

申请人： Ingmar Voigt ,  Dime Vitanovski ,  Razvan Ioan Ionasec ,  Alexey Tsymbal ,  Bogdan Georgescu ,  Shaohua Kevin Zhou ,  Martin Huber ,  Dorin Comaniciu

发明人： Ingmar Voigt ,  Dime Vitanovski ,  Razvan Ioan Ionasec ,  Alexey Tsymbal ,  Bogdan Georgescu ,  Shaohua Kevin Zhou ,  Martin Huber ,  Dorin Comaniciu

IPC分类号： G06N5/00 ,  G06G7/60

CPC分类号： G06G7/60 ,  G06N5/00

摘要： A method and system for providing medical decision support based on virtual organ models and learning based discriminative distance functions is disclosed. A patient-specific virtual organ model is generated from medical image data of a patient. One or more similar organ models to the patient-specific organ model are retrieved from a plurality of previously stored virtual organ models using a learned discriminative distance function. The patient-specific valve model can be classified into a first class or a second class based on the previously stored organ models determined to be similar to the patient-specific organ model.

摘要翻译： 公开了一种基于虚拟器官模型和基于学习的辨别距离函数提供医学决策支持的方法和系统。 从患者的医学图像数据生成患者特异性虚拟器官模型。 使用学习的鉴别距离函数从多个先前存储的虚拟器官模型检索与患者特异器官模型的一个或多个相似的器官模型。 基于先前存储的被确定为类似于患者特异器官模型的器官模型，患者特异性瓣膜模型可以分为第一类或第二类。

公开(公告)号：US08538109B2

公开(公告)日：2013-09-17

申请号：US12724612

申请日：2010-03-16

申请人： Dime Vitanovski ,  Razvan Ioan Ionasec ,  Bogdan Georgescu ,  Martin Huber ,  Dorin Comaniciu

发明人： Dime Vitanovski ,  Razvan Ioan Ionasec ,  Bogdan Georgescu ,  Martin Huber ,  Dorin Comaniciu

IPC分类号： G06T7/00

CPC分类号： G06K9/4638 ,  G06K9/6224 ,  G06K2209/051 ,  G06T7/0012 ,  G06T2207/10076 ,  G06T2207/20081 ,  G06T2207/20108 ,  G06T2207/30048 ,  G06T2207/30101

摘要： A method and system for modeling the pulmonary trunk in 4D image data, such as 4D CT data, and model-based percutaneous pulmonary valve implantation (PPVI) intervention is disclosed. A patient-specific dynamic pulmonary trunk data is generated from 4D image data of a patient. The patient is automatically classified as suitable for PPVI intervention or not suitable for PPVI intervention based on the generated patient-specific dynamic pulmonary trunk model.

摘要翻译： 公开了4D图像数据（如4D CT数据）和基于模型的经皮肺动脉瓣植入（PPVI）干预对肺动脉干线进行建模的方法和系统。 从患者的4D图像数据生成患者特异的动态肺动脉干数据。 根据生成的患者特异性动态肺动脉模型，患者自动分类为适合PPVI干预或不适合PPVI干预。

公开(公告)号：US20100239147A1

公开(公告)日：2010-09-23

申请号：US12724612

申请日：2010-03-16

申请人： Dime Vitanovski ,  Razvan Ioan Ionasec ,  Bogdan Georgescu ,  Martin Huber ,  Dorin Comaniciu

发明人： Dime Vitanovski ,  Razvan Ioan Ionasec ,  Bogdan Georgescu ,  Martin Huber ,  Dorin Comaniciu

IPC分类号： G06K9/00

CPC分类号： G06K9/4638 ,  G06K9/6224 ,  G06K2209/051 ,  G06T7/0012 ,  G06T2207/10076 ,  G06T2207/20081 ,  G06T2207/20108 ,  G06T2207/30048 ,  G06T2207/30101

摘要： A method and system for modeling the pulmonary trunk in 4D image data, such as 4D CT data, and model-based percutaneous pulmonary valve implantation (PPVI) intervention is disclosed. A patient-specific dynamic pulmonary trunk data is generated from 4D image data of a patient. The patient is automatically classified as suitable for PPVI intervention or not suitable for PPVI intervention based on the generated patient-specific dynamic pulmonary trunk model.

摘要翻译： 公开了4D图像数据（如4D CT数据）和基于模型的经皮肺动脉瓣植入（PPVI）干预对肺动脉干线进行建模的方法和系统。 从患者的4D图像数据生成患者特异的动态肺动脉干数据。 根据生成的患者特异性动态肺动脉模型，患者自动分类为适合PPVI干预或不适合PPVI干预。

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

IPC分类号： G06K9/00 ,  G06K9/46 ,  G06T7/11 ,  G06T7/143

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.

公开(公告)号：US20080027917A1

公开(公告)日：2008-01-31

申请号：US11767920

申请日：2007-06-25

申请人： Saikat Mukherjee ,  Shaohua Kevin Zhou ,  Xiang Zhou ,  Martin Huber ,  Jorg Freund ,  Volker Tresp ,  Sonja Zillner ,  Alok Gupta ,  Dorin Comaniciu

发明人： Saikat Mukherjee ,  Shaohua Kevin Zhou ,  Xiang Zhou ,  Martin Huber ,  Jorg Freund ,  Volker Tresp ,  Sonja Zillner ,  Alok Gupta ,  Dorin Comaniciu

IPC分类号： G06F17/30

CPC分类号： G06F16/583 ,  G06N5/022

摘要： A computer-implemented system for searching a plurality of images for an image of interest including a database of semantic image representations corresponding to the plurality of images, wherein the semantic image representations link a semantic model of clinical properties, a syntactic model of high level image properties and an image vocabulary of low level image properties, a set of queries associated with the semantic image representations, and a semantic search engine, embodied as computer readable code executed by a processor, for receiving a search query, selecting at least one of the set of queries based on the search query, and searching the plurality of images for the image of interest by comparing the plurality of images against the semantic image representations associated with a selected query.

摘要翻译： 一种用于搜索感兴趣的图像的多个图像的计算机实现的系统，包括与多个图像相对应的语义图像表示的数据库，其中所述语义图像表示链接临床属性的语义模型，高级图像的句法模型 低级图像属性的属性和图像词汇表，与语义图像表示相关联的一组查询，以及体现为由处理器执行的计算机可读代码的语义搜索引擎，用于接收搜索查询，选择至少一个 基于搜索查询的查询集合，以及通过将多个图像与与所选择的查询相关联的语义图像表示进行比较来搜索多个图像中的感兴趣图像。

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。

公开(公告)号：US20100094874A1

公开(公告)日：2010-04-15

申请号：US12457592

申请日：2009-06-16

申请人： Martin Huber ,  Alexey Tsymbal ,  Sonja Zillner

发明人： Martin Huber ,  Alexey Tsymbal ,  Sonja Zillner

IPC分类号： G06F17/30

CPC分类号： G06F19/325 ,  G06F16/93 ,  G06Q50/22 ,  G16H50/20

摘要： A method and an apparatus retrieve additional information regarding a patient record. Applying the subject-matter of the present invention clinical experts, such as doctors, can be provided with domain specific background knowledge, and can, hence, be supported in making decisions regarding the treatment of patients. Said additional information can be structured and visualised, which makes the retrieved information understandable also for persons without advanced knowledge regarding data processing. The retrieval of additional information is accomplished by a comparison of data being stored in the patient record and data being stored in a predefined textual resource, such as a ontology, and an identification of further terms describing attributes of the patient record. This finds application in supporting the diagnosis of patients and healthcare related information retrieval tasks.

摘要翻译： 一种方法和装置检索关于病人记录的附加信息。 应用本发明的主题，诸如医生的临床专家可以被提供具有领域特异性的背景知识，并且因此可以被支持来做出关于患者治疗的决定。 所述附加信息可以被构造和可视化，这使得检索到的信息也对于没有关于数据处理的高级知识的人也是可以理解的。 通过比较存储在患者记录中的数据和存储在预定义的文本资源（例如本体）中的数据以及描述患者记录的属性的进一步的术语的标识来实现附加信息的检索。 这可以应用于支持患者的诊断和与医疗相关的信息检索任务。

公开(公告)号：US09805473B2

公开(公告)日：2017-10-31

申请号：US12558736

申请日：2009-09-14

申请人： Michael Wels ,  Gustavo Henrique Monteiro de Barros Carneiro ,  Martin Huber ,  Dorin Comaniciu ,  Yefeng Zheng

发明人： Michael Wels ,  Gustavo Henrique Monteiro de Barros Carneiro ,  Martin Huber ,  Dorin Comaniciu ,  Yefeng Zheng

IPC分类号： G06T7/143 ,  G06T7/11

CPC分类号： G06T7/143 ,  G06T7/11 ,  G06T2207/10088 ,  G06T2207/30016

摘要： A method and system for segmenting multiple brain structures in 3D magnetic resonance (MR) images is disclosed. After intensity standardization of a 3D MR image, a meta-structure including center positions of multiple brain structures is detected in the 3D MR image. The brain structures are then individually segmented using marginal space learning (MSL) constrained by the detected meta-structure.

公开(公告)号：US09025841B2

公开(公告)日：2015-05-05

申请号：US12946909

申请日：2010-11-16

申请人： Michael Wels ,  Michael Suehling ,  Michael Kelm ,  Sascha Seifert ,  Maria Jimena Costa ,  Alexander Cavallaro ,  Martin Huber ,  Dorin Comaniciu

发明人： Michael Wels ,  Michael Suehling ,  Michael Kelm ,  Sascha Seifert ,  Maria Jimena Costa ,  Alexander Cavallaro ,  Martin Huber ,  Dorin Comaniciu

IPC分类号： G06K9/00 ,  G06K9/62 ,  G06T7/00

CPC分类号： G06K9/6207 ,  G06T7/0012 ,  G06T7/12 ,  G06T7/149 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/30081

摘要： A method and system for fully automatic segmentation the prostate in multi-spectral 3D magnetic resonance (MR) image data having one or more scalar intensity values per voxel is disclosed. After intensity standardization of multi-spectral 3D MR image data, a prostate boundary is detected in the multi-spectral 3D MR image data using marginal space learning (MSL). The detected prostate boundary is refined using one or more trained boundary detectors. The detected prostate boundary can be split into patches corresponding to anatomical regions of the prostate and the detected prostate boundary can be refined using trained boundary detectors corresponding to the patches.

摘要翻译： 公开了一种用于在具有每个体素具有一个或多个标量强度值的多光谱3D磁共振（MR）图像数据中全息自动分割前列腺的方法和系统。 在多光谱3D MR图像数据的强度标准化之后，使用边缘空间学习（MSL）在多光谱3D MR图像数据中检测前列腺边界。 使用一个或多个训练有素的边界检测器来精确检测前列腺边界。 检测到的前列腺边界可以分裂成对应于前列腺的解剖区域的斑块，并且可以使用对应于斑块的训练边界检测器来精细检测检测到的前列腺边界。