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1. 发明授权

US10878219B2 Method and system for artificial intelligence based medical image segmentation 有权
公开(公告)号：US10878219B2
公开(公告)日：2020-12-29
申请号：US16094900
申请日：2017-07-19
申请人： Siemens Healthcare GmbH
发明人： Shaohua Kevin Zhou , Mingqing Chen , Hui Ding , Bogdan Georgescu , Mehmet Akif Gulsun , Tae Soo Kim , Atilla Peter Kiraly , Xiaoguang Lu , Jin-hyeong Park , Puneet Sharma , Shanhui Sun , Daguang Xu , Zhoubing Xu , Yefeng Zheng
IPC分类号： G06K9/00 , G06K9/46 , G06T7/11 , G06N3/04 , G06N3/08 , G06K9/62 , G16H30/40
CPC分类号： G06K9/0014 , G06K9/4628 , G06N3/0445 , G06N3/0454 , G06N3/084 , G06T7/11 , G06K9/6209 , G16H30/40
摘要： Methods and systems for artificial intelligence based medical image segmentation are disclosed. In a method for autonomous artificial intelligence based medical image segmentation, a medical image of a patient is received. A current segmentation context is automatically determined based on the medical image and at least one segmentation algorithm is automatically selected from a plurality of segmentation algorithms based on the current segmentation context. A target anatomical structure is segmented in the medical image using the selected at least one segmentation algorithm.

2. 发明申请

US20170271135A1 MULTIDIMENSIONAL MASS SPECTROMETRY DATA PROCESSING DEVICE 审中-公开
公开(公告)号：US20170271135A1
公开(公告)日：2017-09-21
申请号：US15532534
申请日：2014-12-08
申请人： SHIMADZU CORPORATION
发明人： Shinichi YAMAGUCHI
IPC分类号： H01J49/00 , G06K9/62 , H01J49/26 , G06F19/00
CPC分类号： H01J49/0036 , G01N27/62 , G06K9/6209 , G06K9/6221 , G06K9/6232 , G06K9/6265 , G06K2009/487 , H01J49/26
摘要： For a sample whose components are separated from each other, an MS2 analysis is repeatedly performed by an MS/MS unit to obtain MS2 spectra. Peak information is collected from the MS2 spectrum acquired for each micro area on a two-dimensional chromatogram. A principal component analysis processor performs a principal component analysis on the collected peak information to determine a principal component score reflecting an overall tendency of the peak pattern of the MS2 spectra. A factor loading calculator computes a factor loading for each m/z from the principal component score. A correlation information calculator computes the coefficient of correlation between each MS2 spectrum and the factor loadings. Since the MS2 spectra reflect the chemical structure of a contained component, a two-dimensional image created based on the coefficient of correlation shows the distribution of a component having a similar chemical structure to a component which is characteristically contained in the sample.

3. 发明授权

US09761060B2 Parameterized model of 2D articulated human shape 有权
公开(公告)号：US09761060B2
公开(公告)日：2017-09-12
申请号：US15342225
申请日：2016-11-03
申请人： Brown University
发明人： Michael J. Black , Oren Freifeld , Alexander W. Weiss , Matthew M. Loper , Peng Guan
IPC分类号： G06T15/00 , G06T19/20 , G06T17/00 , G06T17/20 , G06K9/00 , G06K9/48 , G06T7/13 , G06T7/149 , G06T7/00 , G06T15/20 , G06K9/62 , G06T3/40 , G06T3/60 , G06T11/60
CPC分类号： G06T19/20 , G06K9/00214 , G06K9/00369 , G06K9/48 , G06K9/6209 , G06T3/40 , G06T3/60 , G06T7/13 , G06T7/149 , G06T11/60 , G06T15/20 , G06T17/00 , G06T17/20 , G06T2207/20081 , G06T2207/30196 , G06T2210/16
摘要： Disclosed are computer-readable devices, systems and methods for generating a model of a clothed body. The method includes generating a model of an unclothed human body, the model capturing a shape or a pose of the unclothed human body, determining two-dimensional contours associated with the model, and computing deformations by aligning a contour of a clothed human body with a contour of the unclothed human body. Based on the two-dimensional contours and the deformations, the method includes generating a first two-dimensional model of the unclothed human body, the first two-dimensional model factoring the deformations of the unclothed human body into one or more of a shape variation component, a viewpoint change, and a pose variation and learning an eigen-clothing model using principal component analysis applied to the deformations, wherein the eigen-clothing model classifies different types of clothing, to yield a second two-dimensional model of a clothed human body.

4. 发明申请

US20170243397A1 SHAPE-BASED REGISTRATION FOR NON-RIGID OBJECTS WITH LARGE HOLES 审中-公开
公开(公告)号：US20170243397A1
公开(公告)日：2017-08-24
申请号：US15441166
申请日：2017-02-23
申请人： VanGogh Imaging, Inc.
发明人： Xin Hou , Yasmin Jahir , Jun Yin
IPC分类号： G06T17/10 , G06T5/00 , G06K9/46 , G06T17/20
CPC分类号： G06K9/6209 , G06K9/00214 , G06K9/469 , G06K9/6892 , G06T17/00 , G06T19/20 , G06T2210/56 , G06T2219/2021
摘要： Described herein are methods and systems for closed-form 3D model generation of non-rigid complex objects from scans with large holes. A computing device receives (i) a partial scan of a non-rigid complex object captured by a sensor coupled to the computing device; (ii) a partial 3D model corresponding to the object, and (iii) a whole 3D model corresponding to the object, wherein the partial 3D scan and the partial 3D model each includes one or more large holes. The device performs a rough match on the partial 3D model and changes the whole 3D model using the rough match to generate a deformed 3D model. The device refines the deformed 3D model using a deformation graph, reshapes the refined deformed 3D model to have greater detail, and adjusts the whole 3D model according to the reshaped 3D model to generate a closed-form 3D model that closes holes in the scan.

5. 发明授权

US09740957B2 Learning pixel visual context from object characteristics to generate rich semantic images 有权
公开(公告)号：US09740957B2
公开(公告)日：2017-08-22
申请号：US14473096
申请日：2014-08-29
申请人： Definiens AG
发明人： Olivier Pauly , Nicolas Brieu , Guenter Schmidt , Johannes Zimmermann , Gerd Binnig
IPC分类号： G06K7/00 , G06K9/46 , G06K9/00 , G06T7/00 , G06K9/62
CPC分类号： G06K9/0014 , G06K9/4628 , G06K9/4642 , G06K9/4652 , G06K9/6209 , G06K9/6212 , G06K9/6282 , G06T7/0012
摘要： Both object-oriented analysis and the faster pixel-oriented analysis are used to recognize patterns in an image of stained tissue. Object-oriented image analysis is used to segment a small portion of the image into object classes. Then the object class to which each pixel in the remainder of the image most probably belongs is determined using decision trees with pixelwise descriptors. The pixels in the remaining image are assigned object classes without segmenting the remainder of the image into objects. After the small portion is segmented into object classes, characteristics of object classes are determined. The pixelwise descriptors describe which pixels are associated with particular object classes by matching the characteristics of object classes to the comparison between pixels at predetermined offsets. A pixel heat map is generated by giving each pixel the color assigned to the object class that the pixelwise descriptors indicate is most probably associated with that pixel.

6. 发明申请

US20170069102A1 PARAMETERIZED MODEL OF 2D ARTICULATED HUMAN SHAPE 有权
标题翻译：二维人造形状参数化模型
公开(公告)号：US20170069102A1
公开(公告)日：2017-03-09
申请号：US15342225
申请日：2016-11-03
申请人： Brown University
发明人： Michael J. Black , Oren Freifeld , Alexander W. Weiss , Matthew M. Loper , Peng Guan
IPC分类号： G06T7/00 , G06K9/00 , G06K9/48 , G06T19/20 , G06T15/20
CPC分类号： G06T19/20 , G06K9/00214 , G06K9/00369 , G06K9/48 , G06K9/6209 , G06T3/40 , G06T3/60 , G06T7/13 , G06T7/149 , G06T11/60 , G06T15/20 , G06T17/00 , G06T17/20 , G06T2207/20081 , G06T2207/30196 , G06T2210/16
摘要： Disclosed are computer-readable devices, systems and methods for generating a model of a clothed body. The method includes generating a model of an unclothed human body, the model capturing a shape or a pose of the unclothed human body, determining two-dimensional contours associated with the model, and computing deformations by aligning a contour of a clothed human body with a contour of the unclothed human body. Based on the two-dimensional contours and the deformations, the method includes generating a first two-dimensional model of the unclothed human body, the first two-dimensional model factoring the deformations of the unclothed human body into one or more of a shape variation component, a viewpoint change, and a pose variation and learning an eigen-clothing model using principal component analysis applied to the deformations, wherein the eigen-clothing model classifies different types of clothing, to yield a second two-dimensional model of a clothed human body.
摘要翻译：公开了用于产生穿衣身体模型的计算机可读设备，系统和方法。该方法包括生成未切割的人体的模型，该模型捕获未切割的人体的形状或姿态，确定与模型相关联的二维轮廓，以及通过将穿衣人体的轮廓与未熟的人体轮廓。基于二维轮廓和变形，该方法包括生成未切割的人体的第一二维模型，第一二维模型将未穿过的人体的变形分解为一个或多个形状变化分量，观点变化和姿势变化，并使用应用于变形的主成分分析来学习特征服装模型，其中特征服装模型对不同类型的服装进行分类，以产生穿衣人体的第二二维模型。

7. 发明申请

US20160275342A1 HUMAN FACIAL DETECTION AND RECOGNITION SYSTEM 审中-公开
标题翻译：人类检测与识别系统
公开(公告)号：US20160275342A1
公开(公告)日：2016-09-22
申请号：US15031010
申请日：2014-10-24
申请人： BLUE LINE SECURITY SOLUTIONS, LLC
发明人： Marcos Silva
IPC分类号： G06K9/00 , G06K9/62
CPC分类号： G06K9/00288 , G06K9/00248 , G06K9/00255 , G06K9/00268 , G06K9/4614 , G06K9/6201 , G06K9/6209
摘要： Aspects of the present disclosure provide an image-based face detection and recognition system that processes and/or analyzes portions of an image using “image strips” and cascading classifiers to detect faces and/or various facial features, such an eye, nose, mouth, cheekbone, jaw line, etc.
摘要翻译：本公开的方面提供了一种基于图像的面部检测和识别系统，其使用“图像条”和级联分类器处理和/或分析图像的部分以检测面部和/或各种面部特征，例如眼睛，鼻子，嘴，ek骨，颚线等

8. 发明申请

US20150310612A1 DISCRIMINATING BETWEEN NORMAL AND ABNORMAL LEFT VENTRICLES IN ECHOCARDIOGRAPHY 有权
标题翻译：在正常和异常的左心室之间的歧视
公开(公告)号：US20150310612A1
公开(公告)日：2015-10-29
申请号：US14262780
申请日：2014-04-27
申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION
发明人： David Beymer , Patrick McNeillie , Tanveer Syeda-Mahmood , Quan Wang
IPC分类号： G06T7/00 , G06K9/62
CPC分类号： G06T7/0012 , A61B8/0883 , A61B8/5223 , G06K9/4604 , G06K9/52 , G06K9/6201 , G06K9/6207 , G06K9/6209 , G06K9/6218 , G06K9/6267 , G06K9/6269 , G06K2009/4666 , G06T7/0014 , G06T7/11 , G06T7/13 , G06T7/60 , G06T7/74 , G06T2207/10132 , G06T2207/20021 , G06T2207/20112 , G06T2207/30048
摘要： Embodiments of the present invention relate to discriminating between normal and abnormal left ventricles in echocardiography. In one embodiment, a method of and computer program product for discriminating between normal and abnormal left ventricles in echocardiography are provided. A first region of a first image of a heart is located. The first region depicts a chamber of the heart. A boundary of the first region is determined. A predetermined shape is compared to the boundary. A first plurality of parameters is determined that, when applied to the predetermined shape, conforms the predetermined shape to the boundary. The first plurality of parameters is provided to a classifier. An indication of normality or abnormality is received from the classifier.
摘要翻译：本发明的实施例涉及在超声心动图中区分正常和异常的左心室。在一个实施例中，提供了用于在超声心动图中区分正常和异常左心室的方法和计算机程序产品。位于心脏第一图像的第一区域。第一个区域描绘了心脏的一个房间。确定第一区域的边界。将预定形状与边界进行比较。确定第一多个参数，当应用于预定形状时，使预定形状符合边界。将第一多个参数提供给分类器。从分类器接收到正常或异常的指示。

9. 发明授权

US09042620B2 Method and system for multi-organ segmentation using learning-based segmentation and level set optimization 有权
标题翻译：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
IPC分类号： G06K9/00 , G06T7/00 , G06K9/62
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。

10. 发明申请

US20140050358A1 METHOD OF FACIAL LANDMARK DETECTION 有权
标题翻译：方法地名检测方法
公开(公告)号：US20140050358A1
公开(公告)日：2014-02-20
申请号：US13997296
申请日：2011-03-31
申请人： Ang Liu , Yangzhou Du , Tao Wang , Jianguo Li , Qiang Li , Yimin Zhang
发明人： Ang Liu , Yangzhou Du , Tao Wang , Jianguo Li , Qiang Li , Yimin Zhang
IPC分类号： G06K9/00
CPC分类号： G06K9/00248 , G06K9/00281 , G06K9/6209
摘要： Detecting facial landmarks in a face detected in an image may be performed by first cropping a face rectangle region of the detected face in the image and generating an integral image based at least in part on the face rectangle region. Next, a cascade classifier may be executed for each facial landmark of the face rectangle region to produce one response image for each facial landmark based at least in part on the integral image. A plurality of Active Shape Model (ASM) initializations may be set up. ASM searching may be performed for each of the ASM initializations based at least in part on the response images, each ASM search resulting in a search result having a cost. Finally, a search result of the ASM searches having a lowest cost function may be selected, the selected search result indicating locations of the facial landmarks in the image.
摘要翻译：可以通过首先裁剪图像中检测到的面部的面部矩形区域并且至少部分地基于面部矩形区域来生成整体图像来检测在图像中检测到的面部中的面部界标。接下来，可以对面部矩形区域的每个面部标记执行级联分类器，以至少部分地基于积分图像来为每个面部标记产生一个响应图像。可以设置多个活动形状模型（ASM）初始化。可以至少部分地基于响应图像来对每个ASM初始化执行ASM搜索，每个ASM搜索导致搜索结果具有成本。最后，可以选择具有最低成本函数的ASM搜索的搜索结果，所选择的搜索结果指示图像中的面部地标的位置。

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