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1. 发明申请

US20150313563A1 REGRESSION FOR PERIODIC PHASE-DEPENDENT MODELING IN ANGIOGRAPHY 有权
标题翻译：周期性依赖相位建模的回归
公开(公告)号：US20150313563A1
公开(公告)日：2015-11-05
申请号：US14266191
申请日：2014-04-30
申请人： Michael Kelm , Peng Wang , Andreas Meyer , Guillaume Dumont
发明人： Michael Kelm , Peng Wang , Andreas Meyer , Guillaume Dumont
IPC分类号： A61B6/00 , A61B6/12 , A61M25/09
CPC分类号： A61B6/481 , A61B6/12 , A61M25/09 , G06T7/277 , G06T2207/10016 , G06T2207/10121 , G06T2207/30021 , G06T2207/30101
摘要： A periodic time-varying model of a guide wire, other device, or vessel is created from higher contrast CINE acquisition. A multivariate curve regression is used for periodic, phase dependent motion modeling. This model is then used to locate or track the guide wire, other device or vessel in lower contrast fluoroscopy images during intervention.
摘要翻译：引导线，其他设备或容器的周期时变模型是由较高对比度的CINE采集创建的。多变量曲线回归用于周期性，相位运动建模。然后，该模型用于在干预期间在较低对比度透视图像中定位或跟踪导丝，其他装置或血管。

2. 发明申请

US20120321174A1 Image Processing Using Random Forest Classifiers 有权
标题翻译：使用随机森林分类器的图像处理
公开(公告)号：US20120321174A1
公开(公告)日：2012-12-20
申请号：US13160744
申请日：2011-06-15
申请人： Alexey Tsymbal , Michael Kelm , Maria Jimena Costa , Shaohua Kevin Zhou , Dorin Comaniciu , Yefeng Zheng , Alexander Schwing
发明人： Alexey Tsymbal , Michael Kelm , Maria Jimena Costa , Shaohua Kevin Zhou , Dorin Comaniciu , Yefeng Zheng , Alexander Schwing
IPC分类号： G06K9/62
CPC分类号： G06K9/6219 , G06F17/30247 , G06F17/30268 , G06K9/6263 , G06K9/6282
摘要： A method of performing image retrieval includes training a random forest RF classifier based on low-level features of training images and a high-level feature, using similarity values generated by the RF classifier to determine a subset of the training images that are most similar to one another, and classifying input images for the high-level feature using the RF classifier and the determined subset of images.
摘要翻译：执行图像检索的方法包括基于训练图像的低级特征和高级特征来训练随机森林RF分类器，使用由RF分类器生成的相似性值来确定最相似的训练图像的子集并且使用RF分类器和确定的图像子集对高级特征的输入图像进行分类。

3. 发明申请

US20110116698A1 Method and System for Segmentation of the Prostate in 3D Magnetic Resonance Images 有权
标题翻译：三维磁共振图像前列腺分割方法与系统
公开(公告)号：US20110116698A1
公开(公告)日：2011-05-19
申请号：US12946909
申请日：2010-11-16
申请人： Michael Weis , Michael Suehling , Michael Kelm , Sascha Seifert , Maria Jimena Costa , Alexander Cavallaro , Martin Huber , Dorin Comaniciu
发明人： Michael Weis , Michael Suehling , Michael Kelm , Sascha Seifert , Maria Jimena Costa , Alexander Cavallaro , Martin Huber , Dorin Comaniciu
IPC分类号： G06K9/00
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。

4. 发明申请

US20110064291A1 Method and System for Detection 3D Spinal Geometry Using Iterated Marginal Space Learning 有权
标题翻译：使用迭代边缘空间学习检测3D脊柱几何的方法和系统
公开(公告)号：US20110064291A1
公开(公告)日：2011-03-17
申请号：US12794850
申请日：2010-06-07
申请人： Michael Kelm , Shaohua Kevin Zhou , Yefeng Zheng , Michael Suehling
发明人： Michael Kelm , Shaohua Kevin Zhou , Yefeng Zheng , Michael Suehling
IPC分类号： G06K9/00
CPC分类号： G06T7/0087 , G06K2209/055 , G06T7/143 , G06T2207/20076 , G06T2207/20081 , G06T2207/30012
摘要： A method and apparatus for automatic detection and labeling of 3D spinal geometry is disclosed. Cervical, thoracic, and lumbar spine regions are detected in a 3D image. Intervertebral disk candidates are detected in each of the spine regions using iterative marginal space learning (MSL). Using a global probabilistic spine model, a separate one of the intervertebral disk candidates is selected for each of a plurality of labeled intervertebral disk locations.
摘要翻译：公开了一种用于3D脊柱几何的自动检测和标记的方法和装置。在3D图像中检测到子宫颈，胸部和腰椎区域。使用迭代边缘空间学习（MSL）在每个脊柱区域中检测椎间盘候选物。使用全局概率脊柱模型，为多个标记的椎间盘位置中的每一个选择椎间盘候选物中的单独的一个。

5. 发明授权

US09642586B2 Computer-aided analysis of medical images 有权
公开(公告)号：US09642586B2
公开(公告)日：2017-05-09
申请号：US14462014
申请日：2014-08-18
申请人： Michael Kelm , Yefeng Zheng
发明人： Michael Kelm , Yefeng Zheng
IPC分类号： G06K9/00 , A61B6/00 , A61B5/00 , A61B6/03 , G06T7/00
CPC分类号： A61B6/5235 , A61B5/7267 , A61B6/032 , A61B6/503 , A61B6/504 , A61B6/508 , A61B6/5217 , G06T7/0012 , G06T7/11 , G06T7/174 , G06T2207/10081 , G06T2207/30101 , G06T2207/30172
摘要： A pair of medical images is analyzed, the pair including a first image, which is a contrasted scan of a part in a human or animal body, and a second image, which is a native scan of the same part of the human or animal body. Anatomic structures are identified within both the first image and the second image. By using those anatomic structures, centerlines of vessels in the first image are mapped to the second image. Candidate calcified plaques are extracted in the second image, and calcified plaques out of the candidate calcified plaques are identified by a machine learning classifier. The positional information of the centerlines in the second image is used for extracting the candidate calcified plaques in the second image and/or for identifying the calcified plaques out of the candidate calcified plaques by the machine learning classifier.

6. 发明申请

US20160045180A1 Computer-Aided Analysis of Medical Images 有权
标题翻译：医学图像的计算机辅助分析
公开(公告)号：US20160045180A1
公开(公告)日：2016-02-18
申请号：US14462014
申请日：2014-08-18
申请人： Michael Kelm , Yefeng Zheng
发明人： Michael Kelm , Yefeng Zheng
IPC分类号： A61B6/00 , A61B6/03 , A61B5/00
CPC分类号： A61B6/5235 , A61B5/7267 , A61B6/032 , A61B6/503 , A61B6/504 , A61B6/508 , A61B6/5217 , G06T7/0012 , G06T7/11 , G06T7/174 , G06T2207/10081 , G06T2207/30101 , G06T2207/30172
摘要： A pair of medical images is analysed, the pair including a first image, which is a contrasted scan of a part in a human or animal body, and a second image, which is a native scan of the same part of the human or animal body. Anatomic structures are identified within both the first image and the second image. By using those anatomic structures, centerlines of vessels in the first image are mapped to the second image. Candidate calcified plaques are extracted in the second image, and calcified plaques out of the candidate calcified plaques are identified by a machine learning classifier. The positional information of the centerlines in the second image is used for extracting the candidate calcified plaques in the second image and/or for identifying the calcified plaques out of the candidate calcified plaques by the machine learning classifier.
摘要翻译：分析一对医学图像，该对包括第一图像，其是人或动物体中的一部分的对比扫描，以及第二图像，其是人或动物体的相同部分的本机扫描。在第一图像和第二图像内都识别解剖结构。通过使用那些解剖结构，第一个图像中血管的中心线被映射到第二个图像。在第二图像中提取候选钙化斑块，并且通过机器学习分类器鉴定候选钙化斑块中的钙化斑块。第二图像中的中心线的位置信息用于提取第二图像中的候选钙化斑块和/或用于通过机器学习分类器识别候选钙化斑块中的钙化斑块。

7. 发明授权

US09113781B2 Method and system for on-site learning of landmark detection models for end user-specific diagnostic medical image reading 有权
标题翻译：用于最终用户特定诊断医学图像阅读的地标检测模型的现场学习方法和系统
公开(公告)号：US09113781B2
公开(公告)日：2015-08-25
申请号：US13761263
申请日：2013-02-07
申请人： Michael Wels , Michael Kelm , Michael Suehling , Shaohua Kevin Zhou
发明人： Michael Wels , Michael Kelm , Michael Suehling , Shaohua Kevin Zhou
IPC分类号： G06K9/00 , A61B5/00 , A61B6/00 , G06K9/62 , G06F19/00
CPC分类号： A61B5/0033 , A61B6/00 , A61B6/025 , A61B6/502 , G06F19/00 , G06F19/321 , G06K9/00 , G06K9/6255 , G06K2209/05
摘要： A method and system for on-line learning of landmark detection models for end-user specific diagnostic image reading is disclosed. A selection of a landmark to be detected in a 3D medical image is received. A current landmark detection result for the selected landmark in the 3D medical image is determined by automatically detecting the selected landmark in the 3D medical image using a stored landmark detection model corresponding to the selected landmark or by receiving a manual annotation of the selected landmark in the 3D medical image. The stored landmark detection model corresponding to the selected landmark is then updated based on the current landmark detection result for the selected landmark in the 3D medical image. The landmark selected in the 3D medical image can be a set of landmarks defining a custom view of the 3D medical image.
摘要翻译：公开了一种用于在线学习用于最终用户特定诊断图像读取的地标检测模型的方法和系统。接收在3D医疗图像中检测的地标的选择。通过使用与所选择的地标相对应的存储的地标检测模型自动检测3D医学图像中的所选地标来确定3D医学图像中所选择的地标的当前地标检测结果，或者通过接收所选地标的手动注释 3D医学图像。然后基于3D医学图像中所选地标的当前地标检测结果来更新对应于所选地标的所存储的地标检测模型。在3D医学图像中选择的地标可以是定义3D医学图像的定制视图的一组地标。

8. 发明申请

US20110235887A1 Computer-Aided Evaluation Of An Image Dataset 有权
标题翻译：计算机辅助评估图像数据集
公开(公告)号：US20110235887A1
公开(公告)日：2011-09-29
申请号：US13053263
申请日：2011-03-22
申请人： Rüdiger Bertsch , Roland Brill , Alexander Cavallaro , Maria Jimena Costa , Martin Huber , Michael Kelm , Helmut König , Sascha Seifert , Michael Wels
发明人： Rüdiger Bertsch , Roland Brill , Alexander Cavallaro , Maria Jimena Costa , Martin Huber , Michael Kelm , Helmut König , Sascha Seifert , Michael Wels
IPC分类号： G06K9/00
CPC分类号： A61B6/5217 , A61B5/055 , A61B5/415 , A61B5/418 , A61B5/4842 , G06F19/00 , G06F19/321 , G16H50/20
摘要： A method and system for the diagnosis of 3D images are disclosed, which significantly cuts the time required for the diagnosis. The 3D images are for example an image volume dataset of a magnetic resonance tomography system which is saved in an RIS or PACS system. In at least one embodiment, the diagnostic finding are partially automatically generated, and details of the position, size and change in pathological structures are compared to previous diagnostic findings are generated automatically. As a result of this automation the diagnostic work of radiologists is significantly reduced.
摘要翻译：公开了用于诊断3D图像的方法和系统，其显着地缩短了诊断所需的时间。 3D图像例如是保存在RIS或PACS系统中的磁共振断层摄影系统的图像体积数据集。在至少一个实施例中，部分地自动生成诊断发现，并且自动地生成病理结构的位置，大小和变化的细节与先前的诊断结果进行比较。由于这种自动化，放射科医师的诊断工作显着减少。

9. 发明申请

US20110182493A1 METHOD AND A SYSTEM FOR IMAGE ANNOTATION 审中-公开
标题翻译：方法和一种用于图像注释的系统
公开(公告)号：US20110182493A1
公开(公告)日：2011-07-28
申请号：US12711363
申请日：2010-02-24
申请人： Martin HUBER , Michael Kelm , Sascha Seifert
发明人： Martin HUBER , Michael Kelm , Sascha Seifert
IPC分类号： G06K9/00 , G06K9/34 , G06F3/048
CPC分类号： G06F19/321 , G16H15/00
摘要： A method and a system are disclosed for image annotation of images, in particular two- and three-dimensional medical images. In at least one embodiment, the image annotation system includes an image parser which parses images retrieved from an image database or provided by an image acquisition apparatus and segments each image into image regions. The image can be provided by any kind of image acquisition apparatus such as a digital camera an x-ray apparatus, a computer tomograph or a magnetic resonance scanning apparatus. Each segmented image regions is annotated automatically with annotation data and stored in an annotation database. In at least one embodiment, the system includes at least one user terminal which loads at least one selected image from said image database and retrieved the corresponding annotation data of all segmented image regions of said image from said annotation database for further annotation of the image. The image annotation system, in at least one embodiment, allows for a more efficient and more reliable annotation of images which can be further processed to generate automatically reports for examples of patients in a hospital. The image annotation method and system according to at least one embodiment of the invention, can be used in a wide range of applications in particular of annotation of medical images but also in security systems as well as in the developments of prototypes of complex apparatuses such as automobiles.
摘要翻译：公开了用于图像的图像注释的方法和系统，特别是二维和三维医学图像。在至少一个实施例中，图像注释系统包括图像解析器，其解析从图像数据库检索或由图像获取装置提供的图像，并将每个图像分割成图像区域。该图像可以由诸如数字照相机，X射线设备，计算机断层摄影机或磁共振扫描设备的任何种类的图像采集设备提供。每个分割的图像区域都使用注释数据自动注释并存储在注释数据库中。在至少一个实施例中，系统包括至少一个用户终端，其从所述图像数据库加载至少一个选定的图像，并且从所述注释数据库检索所述图像的所有分割图像区域的对应注释数据，以进一步注释图像。在至少一个实施例中，图像注释系统允许图像的更有效和更可靠的注释，其可被进一步处理以自动生成报告，用于医院中患者的示例。根据本发明的至少一个实施例的图像注释方法和系统可以用于特别是医学图像的注释的广泛应用中，但也可以用于安全系统以及复杂装置的原型开发中，例如汽车

10. 发明申请

US20090310836A1 Automatic Learning of Image Features to Predict Disease 有权
标题翻译：自动学习图像特征预测疾病
公开(公告)号：US20090310836A1
公开(公告)日：2009-12-17
申请号：US12427974
申请日：2009-04-22
申请人： Arun Krishnan , Xiang Zhou , Martin Huber , Michael Kelm , Joerg Freund
发明人： Arun Krishnan , Xiang Zhou , Martin Huber , Michael Kelm , Joerg Freund
IPC分类号： G06K9/62 , G06K9/00
CPC分类号： G06T7/11 , G06F19/00 , G06T7/0012 , G06T2207/20081 , G16H50/70
摘要： A method for training a computer system for automatic detection of regions of interest includes receiving patient records. For each of the received patient records a text field and a medical image are identified from within the patient record and the medical image is automatically segmented to identify a structure of interest. The text field is searched for one or more keywords indicative of a particular abnormality associated with the structure of interest. The medical image is added to a grouping representing the particular abnormality when the text field indicates that the patient has the particular abnormality and the medical image is added to a grouping representing the absence of the particular abnormality when the text field does not indicate that the patient has the particular abnormality. The groupings of medical images are used to automatically train a computer system for the subsequent detection of the particular abnormality.
摘要翻译：用于训练用于感兴趣区域的自动检测的计算机系统的方法包括接收患者记录。对于每个接收到的患者记录，从患者记录中识别文本字段和医学图像，并且医疗图像被自动分段以识别感兴趣的结构。搜索文本字段以查找指示与感兴趣结构相关联的特定异常的一个或多个关键字。当文本字段指示患者具有特定异常并将医学图像添加到代表不存在特定异常的分组时，医疗图像被添加到表示特定异常的分组中，当文本字段没有指示患者有特殊的异常。医学图像的分组用于自动训练计算机系统以便随后检测特定的异常。

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