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41. 发明授权

EP3053139B1 LINE-BASED IMAGE REGISTRATION AND CROSS-IMAGE ANNOTATION DEVICES, SYSTEMS AND METHODS 有权
公开(公告)号：EP3053139B1
公开(公告)日：2020-10-21
申请号：EP14780824.0
申请日：2014-09-30
申请人： Ventana Medical Systems, Inc.
发明人： CHUKKA, Srinivas , SARKAR, Anindya , YUAN, Quan
IPC分类号： G06K9/00 , G06T3/00 , G06T7/33

42. 发明授权

EP3178035B1 IMAGE ANALYSIS SYSTEM USING CONTEXT FEATURES 有权
公开(公告)号：EP3178035B1
公开(公告)日：2020-09-23
申请号：EP15748219.1
申请日：2015-08-04
申请人： Ventana Medical Systems, Inc.
发明人： CHUKKA, Srinivas , NIE, Yao
IPC分类号： G06K9/00 , G06K9/62

43. 发明公开

EP3550514A1 GROUP SPARSITY MODEL FOR IMAGE UNMIXING 有权
公开(公告)号：EP3550514A1
公开(公告)日：2019-10-09
申请号：EP19170902.1
申请日：2015-02-23
申请人： Ventana Medical Systems, Inc.
发明人： CHUKKA, Srinivas , CHEN, Ting
IPC分类号： G06T7/00 , G06K9/00
摘要： Systems and/or methods described herein relate to unmixing more than three stains, while preserving the biological constraints of the biomarkers. Unlimited numbers of markers may be unmixed from a limited-channel image, such as an RGB image, without adding any mathematical complicity to the model. Known co-localization information of different biomarkers within the same tissue section enables defining fixed upper bounds for the number of stains at one pixel. A group sparsity model may be leveraged to explicitly model the fractions of stain contributions from the co-localized biomarkers into one group to yield a least squares solution within the group. A sparse solution may be obtained among the groups to ensure that only a small number of groups with a total number of stains being less than the upper bound are activated.

44. 发明授权

EP3155592B1 PREDICTING BREAST CANCER RECURRENCE DIRECTLY FROM IMAGE FEATURES COMPUTED FROM DIGITIZED IMMUNOHISTOPATHOLOGY TISSUE SLIDES 有权
公开(公告)号：EP3155592B1
公开(公告)日：2019-09-11
申请号：EP15730991.5
申请日：2015-06-10
申请人： Leland Stanford Junior University , Ventana Medical Systems, Inc.
发明人： CHUKKA, Srinivas , BARNES, Michael , KNOWLES, David
IPC分类号： G06T7/00 , G06K9/00 , C12Q1/68 , G01N21/64 , G06K9/62

45. 发明授权

EP3371776B1 COMPUTER-IMPLEMENTED COMPOSITE TISSUE IMAGE WITH REAL-TIME ADJUSTABLE INTERFACE 有权转让
公开(公告)号：EP3371776B1
公开(公告)日：2019-08-28
申请号：EP16790972.0
申请日：2016-11-02
申请人： Ventana Medical Systems, Inc.
发明人： BREDNO, Joerg , CHUKKA, Srinivas
IPC分类号： G06T7/33 , G06T11/00

46. 发明授权

EP3389011B1 IMAGE ANALYSIS SYSTEM FOR DETECTING EDGES OF A NUCLEUS 有权
公开(公告)号：EP3389011B1
公开(公告)日：2019-06-12
申请号：EP18170775.3
申请日：2015-06-30
申请人： Ventana Medical Systems, Inc.
发明人： CHUKKA, Srinivas , CHEFD'HOTEL, Christophe , WANG, Xiuzhong
IPC分类号： G06T7/12 , G06T7/13 , G06T7/136 , G06T7/194

47. 发明公开

EP3053139A1 LINE-BASED IMAGE REGISTRATION AND CROSS-IMAGE ANNOTATION DEVICES, SYSTEMS AND METHODS 有权
标题翻译： VOLLSCHNITTBILDREGISTRIERUNG UNDBILDÜBERGREIFENDEANNOTATIONSVORRICHTUNGEN，-SYSTEME UND-EXFAHREN
公开(公告)号：EP3053139A1
公开(公告)日：2016-08-10
申请号：EP14780824.0
申请日：2014-09-30
申请人： Ventana Medical Systems, Inc.
发明人： CHUKKA, Srinivas , SARKAR, Anindya , YUAN, Quan
IPC分类号： G06T7/00
摘要： The disclosure relates to devices, systems and methods for image registration and annotation. The devices include computer software products for aligning whole slide digital images on a common grid and transferring annotations from one aligned image to another aligned image on the basis of matching tissue structure. The systems include computer-implemented systems such as work stations and networked computers for accomplishing the tissue-structure based image registration and cross-image annotation. The methods include processes for aligning digital images corresponding to adjacent tissue sections on a common grid based on tissue structure, and transferring annotations from one of the adjacent tissue images to another of the adjacent tissue images. The basis for alignment may be a line-based registration process, wherein sets of lines are computed on the boundary regions computed for the two images, where the boundary regions are obtained using information from two domains—soft-weighted foreground images and gradient magnitude images. The binary mask image, based on whose boundary the line features are computed, may be generated by combining two binary masks—a first binary mask is obtained on thresholding a soft-weighted (continuous valued) foreground image, which is computed based on the stain content in an image, while a second binary mask is obtained after thresholding a gradient magnitude domain image, where the gradient is computed from the grayscale image obtained from the color image.
摘要翻译：本公开涉及用于图像注册和注释的装置，系统和方法。这些设备包括用于在公共网格上对齐整个幻灯片数字图像的计算机软件产品，并且基于匹配的组织结构将注释从一个对齐的图像传送到另一个对齐的图像。这些系统包括计算机实现的系统，例如工作站和网络计算机，用于完成基于组织结构的图像配准和跨图像注释。所述方法包括基于组织结构对准在公共网格上对应于相邻组织部分的数字图像并将注释从相邻组织图像之一转移到相邻组织图像中的另一组织的处理。对准的基础可以是基于行的注册过程，其中在针对两个图像计算的边界区域上计算行集合，其中使用来自两个域的信息获得边界区域 - 软加权前景图像和梯度幅度图像。可以通过组合两个二进制掩码来生成基于其边界计算线特征的二进制掩码图像 - 在阈值化软加权（连续值）前景图像上获得第一二进制掩码，其基于该染色来计算而在阈值化梯度幅度域图像之后获得第二二进制掩码，其中从从彩色图像获得的灰度图像中计算梯度。

48. 发明公开

EP2973396A2 WHOLE SLIDE IMAGE REGISTRATION AND CROSS-IMAGE ANNOTATION DEVICES, SYSTEMS AND METHODS 有权转让
标题翻译： VOLLSCHNITTBILDREGISTRIERUNG UNDBILDÜBERGREIFENDEANNOTATIONSVORRICHTUNGEN，SYSTEME UND VERFAHREN
公开(公告)号：EP2973396A2
公开(公告)日：2016-01-20
申请号：EP14713778.0
申请日：2014-03-12
申请人： Ventana Medical Systems, Inc.
发明人： CHUKKA, Srinivas , SABATA, Bikash , SARKAR, Anindya , YUAN, Quan
IPC分类号： G06T7/00
CPC分类号： G06T7/0014 , G06K9/00127 , G06K9/46 , G06K9/6201 , G06T7/33 , G06T7/38 , G06T11/60 , G06T2200/24 , G06T2207/30024
摘要： The disclosure relates to devices, systems and methods for image registration and annotation. The devices include computer software products for aligning whole slide digital images on a common grid and transferring annotations from one aligned image to another aligned image on the basis of matching tissue structure. The systems include computer-implemented systems such as work stations and networked computers for accomplishing the tissue-structure based image registration and cross-image annotation. The methods include processes for aligning digital images corresponding to adjacent tissue sections on a common grid based on tissue structure, and transferring annotations from one of the adjacent tissue images to another of the adjacent tissue images.
摘要翻译：本公开涉及用于图像注册和注释的装置，系统和方法。这些设备包括用于在公共网格上对齐整个幻灯片数字图像的计算机软件产品，并且基于匹配的组织结构将注释从一个对齐的图像传送到另一个对齐的图像。这些系统包括计算机实现的系统，例如工作站和网络计算机，用于完成基于组织结构的图像配准和跨图像注释。所述方法包括基于组织结构对准在公共网格上对应于相邻组织部分的数字图像并将注释从相邻组织图像之一转移到相邻组织图像中的另一组织的处理。

49. 发明公开

EP3729369A2 COMPUTATIONAL PATHOLOGY APPROACH FOR RETROSPECTIVE ANALYSIS OF TISSUE-BASED COMPANION DIAGNOSTIC DRIVEN CLINICAL TRIAL STUDIES 审中-公开
公开(公告)号：EP3729369A2
公开(公告)日：2020-10-28
申请号：EP18833184.7
申请日：2018-12-17
申请人： Ventana Medical Systems, Inc.
发明人： BARNES, Michael , CHEFD'HOTEL, Christophe , CHUKKA, Srinivas
IPC分类号： G06T7/00 , G16H50/30

50. 发明公开

EP3178035A2 IMAGE ANALYSIS SYSTEM USING CONTEXT FEATURES 有权
标题翻译：使用上下文特征的图像分析系统
公开(公告)号：EP3178035A2
公开(公告)日：2017-06-14
申请号：EP15748219.1
申请日：2015-08-04
申请人： Ventana Medical Systems, Inc.
发明人： CHUKKA, Srinivas , NIE, Yao
IPC分类号： G06K9/00 , G06K9/62
摘要： The present disclosure relates, among other things, to an image analysis method for identifying objects belonging to a particular objet class in a digital image of a biological sample. The method may include, among other things, analyzing the digital image for automatically or semi-automatically identifying objects in the digital image; analyzing the digital image for identifying, for each object, a first object feature value of a first object feature of said object; analyzing the digital image for computing one or more first context feature values; inputting both the first object feature value of each of the objects in the digital image and the first context feature value of said digital image into a first classifier; and executing the first classifier.
摘要翻译：本公开涉及一种用于识别属于生物样本的数字图像（102-108）中的特定对象类的对象的图像分析系统，所述系统包括处理器和存储器，所述存储器包括可解释指令，所述可解释指令当被执行时所述处理器使所述处理器执行包括以下的方法： - 分析（602）所述数字图像以自动或半自动地识别所述数字图像中的物体; - 分析（604）数字图像，用于针对每个对象识别所述对象的第一对象特征的第一对象特征值（202,702）; - - 分析（606）用于计算一个或多个第一上下文特征值（204,704）的数字图像，每个第一上下文特征值是第一对象特征值或多个对象中的对象的特征值的导数数字图像或者是数字图像的多个像素的导数; - 将数字图像中的每个对象的第一对象特征值和所述数字图像的第一上下文特征值输入（608）到第一分类器（210,710）中; 以及 - 执行（610）所述第一分类器，所述第一分类器由此使用每个对象的所述第一对象特征值和所述一个或多个第一上下文特征值作为输入以用于针对所述对象自动确定第一似然性（216,714）作为对象类的成员的所述对象的对象。

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