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1.发明公开
公开(公告)号：EP4220564A2
公开(公告)日：2023-08-02
申请号：EP22214786.0
申请日：2016-08-25
摘要： A system and method of displaying of multiple simultaneous views of a same region of a biological tissue sample. Logical instructions are executed by a processor to perform operations such as receiving a plurality of images of the biological tissue sample, converting the plurality of images to a common reference frame based on the individual metadata of each image, and arranging the plurality of images into a display pattern for simultaneous viewing of different aspects of the imaged biological tissue sample on a display screen. The plurality of images is produced by preprocessing images of the biological tissue sample. Each image shows a view mode of a same region of the biological tissue sample, and each image contains metadata that describe spatial orientation, such as the translation, rotation, and magnification, of the image to bring the plurality of images to a common view.
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公开(公告)号：EP3882851A1
公开(公告)日：2021-09-22
申请号：EP21173782.0
申请日：2015-12-30
IPC分类号： G06T7/00
摘要： Described herein are methods for co-expression analysis of multiple markers in a tissue sample comprising: computing a heat map of marker expression for each of a plurality of single marker channel images, wherein each of the plurality of single marker channel images comprise a single marker; identifying one or more candidate regions of interest in each heat map of marker expression; computing overlay masks comprising the identified one or more candidate regions of interest from each heat map of marker expression; determining one or more co-localized regions of interest from the overlay masks; mapping the one or more co-localized regions of interest to a same coordinate position in each of the plurality of single marker channel images; and estimating a number of cells in at least one of the determined one or more co-localized regions of interest in each of the plurality of single marker channel images.
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公开(公告)号：EP3195191B1
公开(公告)日：2020-10-21
申请号：EP15767102.5
申请日：2015-09-03
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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
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5.发明公开
公开(公告)号：EP3241180A1
公开(公告)日：2017-11-08
申请号：EP15820183.0
申请日：2015-12-30
IPC分类号： G06T7/00
CPC分类号： G06T7/0012 , G06F19/00 , G06T7/00 , G06T2207/30024
摘要： Described herein are methods for co-expression analysis of multiple markers in a tissue sample comprising: computing a heat map of marker expression for each of a plurality of single marker channel images, wherein each of the plurality of single marker channel images comprise a single marker; identifying one or more candidate regions of interest in each heat map of marker expression; computing overlay masks comprising the identified one or more candidate regions of interest from each heat map of marker expression; determining one or more co-localized regions of interest from the overlay masks; mapping the one or more co-localized regions of interest to a same coordinate position in each of the plurality of single marker channel images; and estimating a number of cells in at least one of the determined one or more co-localized regions of interest in each of the plurality of single marker channel images.
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公开(公告)号：EP3839812A3
公开(公告)日：2021-09-01
申请号：EP20202876.7
申请日：2015-09-03
摘要： Imaging systems, methods, and apparatuses for automatically identifying fields of view (FOVs) for regions in an image encompassing melanoma is disclosed. In embodiments and in further aspects of the present invention, a computer-implemented method is disclosed for a tumor region based immune score computation. The method, in accordance with the present invention, involves identifying regions, for example, tumor areas or regions around a tumor area, partitioning a whole slide image or portion of a whole slide image into multiple regions related to the tumor, selecting FOVs within each identified region, and computing a number of cells present in each FOV. An immune score and/or immune-related score is generated based on the cells counted in each FOV.
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公开(公告)号：EP3227833A1
公开(公告)日：2017-10-11
申请号：EP15805448.6
申请日：2015-12-03
发明人： BARNES, Michael , CHUKKA, Srinivas , LAFLEUR, Bonnie , XU, Chang
CPC分类号： G06T7/0012 , G01N33/57415 , G01N2800/52 , G01N2800/54 , G06F19/00 , G06K9/00147 , G06K9/6265 , G06T7/11 , G06T7/12 , G06T2207/10056 , G06T2207/30024 , G16H50/30
摘要： The subject disclosure presents systems and computer-implemented methods for providing reliable risk stratification for early-stage cancer patients by predicting a recurrence risk of the patient and to categorize the patient into a high or low risk group. A series of slides depicting serial sections of cancerous tissue are automatically analyzed by a digital pathology system, a score for the sections is calculated, and a Cox proportional hazards regression model is used to stratify the patient into a low or high risk group. The Cox proportional hazards regression model may be used to determine a whole-slide scoring algorithm based on training data comprising survival data for a plurality of patients and their respective tissue sections. The coefficients may differ based on different types of image analysis operations applied to either whole-tumor regions or specified regions within a slide.
摘要翻译： 本公开内容呈现了用于通过预测患者的复发风险并将患者分类为高风险或低风险组来为早期癌症患者提供可靠的风险分层的系统和计算机实现的方法。 描绘癌组织（4'）的连续切片的一系列切片（1'，2'，3'）由数字病理学系统自动分析，计算切片的评分并使用Cox比例风险回归模型 将患者分层为低风险组或高风险组。 可以使用Cox比例风险回归模型来基于包括多个患者及其相应组织切片的生存数据的训练数据来确定整个切片评分算法。 基于应用于载玻片上的全肿瘤区域或指定区域的不同类型的图像分析操作，系数可能不同。
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8.发明公开METHODS, SYSTEMS, AND APPARATUSES FOR QUANTITATIVE ANALYSIS OF HETEROGENEOUS BIOMARKER DISTRIBUTION 审中-公开用于定量分析非均匀生物标记分布的方法，系统和装置
公开(公告)号：EP3227830A1
公开(公告)日：2017-10-11
申请号：EP15813713.3
申请日：2015-12-03
发明人： BARNES, Michael , CHAFIN, David , GARSHA, Karl , GROGAN, Thomas M. , ROBERTS, Esteban , STEVENS, Benjamin , VENTURA, Frank , CHEFD'HOTEL, Christophe , SHANMUGAM, Kandavel , GRAY, Joe , RAMUNNO-JOHNSON, Damien , VU, Tothu (Tania)
CPC分类号： G06T7/0012 , G06K9/00147 , G06K9/3233 , G06K9/4652 , G06K9/6222 , G06T7/11 , G06T7/90 , G06T2207/10036 , G06T2207/10056 , G06T2207/10064 , G06T2207/20032 , G06T2207/20104 , G06T2207/20152 , G06T2207/30024 , G06T2207/30096
摘要： Methods, systems, and apparatuses for detecting and describing heterogeneity in a cell sample are disclosed herein. A plurality of fields of view (FOV) are generated for one or more areas of interest (AOI) within an image of the cell sample are generated. Hyperspectral or multispectral data from each FOV is organized into an image stack containing one or more z-layers, with each z-layer containing intensity data for a single marker at each pixel in the FOV. A cluster analysis is applied to the image stacks, wherein the clustering algorithm groups pixels having a similar ratio of detectable marker intensity across layers of the z-axis, thereby generating a plurality of clusters having similar expression patterns.
摘要翻译： 本文公开了用于检测和描述细胞样品中异质性的方法，系统和装置。 针对细胞样品的图像内的一个或多个感兴趣区域（AOI）生成多个视场（FOV）。 来自每个FOV的高光谱或多光谱数据组织成包含一个或多个z层的图像堆栈，每个z层包含FOV中每个像素处的单个标记的强度数据。 将聚类分析应用于图像堆栈，其中聚类算法对具有跨越z轴的层的可检测标记强度的相似比率的像素进行分组，从而生成具有相似表达模式的多个聚类。
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9.发明公开
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
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公开(公告)号：EP3195191A2
公开(公告)日：2017-07-26
申请号：EP15767102.5
申请日：2015-09-03
摘要： Embodiments disclosed herein are directed, among other things, to imaging systems, methods, and apparatuses for automatically identifying fields of view (FOVs) for regions in an image encompassing tumor are disclosed. In embodiments and in further aspects of the present invention, a computer-implemented method is disclosed for a tumor region based immune score computation. The method, in accordance with the present invention, involves identifying regions, for example, tumor areas or regions around a tumor area, partitioning a whole slide image or portion of a whole slide image into multiple regions related to the tumor, selecting FOVs within each identified region, and computing a number of cells present in each FOV. An immune score and/or immune-related score may be generated based on the cells counted in each FOV.
摘要翻译： 公开了用于自动识别包含黑素瘤的图像中的区域的视野（FOV）的成像系统，方法和设备。 在本发明的实施例和其他方面中，公开了一种用于基于肿瘤区域的免疫分数计算的计算机实现的方法。 根据本发明，该方法涉及识别区域，例如肿瘤区域或肿瘤区域周围的区域，将整个载玻片图像或整个载玻片图像的一部分分成与肿瘤相关的多个区域，在每个区域内选择FOV 识别的区域，并计算每个FOV中存在的细胞的数量。 基于在每个FOV中计数的细胞生成免疫分数和/或免疫相关分数。
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