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1. 发明申请

WO2015124737A1 METHODS, KITS, AND SYSTEMS FOR SCORING THE IMMUNE RESPONSE TO CANCER BY SIMULTANEOUS DETECTION OF CD3, CD8, CD20 AND FOXP3 审中-公开
标题翻译：通过同时检测CD3，CD8，CD20和FOXP3来评估对癌症的免疫应答的方法，试剂盒和系统
公开(公告)号：WO2015124737A1
公开(公告)日：2015-08-27
申请号：PCT/EP2015/053643
申请日：2015-02-20
申请人： VENTANA MEDICAL SYSTEMS, INC. , F. HOFFMANN-LA ROCHE AG
发明人： SEBASTIAO, Noemi , DAY, William , OCHS, Robert , CHUKKA, Srinivas , MARTIN, Jim , BARNES, Michael , BREDNO, Joerg , CHEN, Ting , TUBBS, Alisa , NIE, Yao
IPC分类号： G01N33/574
CPC分类号： G01N33/5743 , G01N15/1463 , G01N33/566 , G01N33/574 , G01N33/57415 , G01N33/6875 , G01N35/00029 , G01N2015/008 , G01N2015/1006 , G01N2015/1486 , G01N2035/00138 , G01N2333/4703 , G01N2333/4739 , G01N2333/4742 , G01N2333/70503 , G01N2333/7051 , G01N2333/70517 , G06F19/20
摘要： This disclosure describes methods, kits, and systems for scoring the immune response to cancer through examination of tissue infiltrating lymphocytes (TILs). Methods of scoring the immune response in cancer using tissue infiltrating lymphocytes include detecting CD3, CD8, CD20, and FoxP3 within the sample and scoring the detection manually or scoring the digital images of the staining with the aid of image analysis and algorithms.
摘要翻译：本公开描述了通过检查组织浸润淋巴细胞（TIL）评分对癌症的免疫应答的方法，试剂盒和系统。使用组织浸润淋巴细胞评估癌症免疫反应的方法包括检测样品中的CD3，CD8，CD20和FoxP3，手动评分检测，或借助图像分析和算法对染色的数字图像进行评分。

2. 发明申请

WO2015052128A1 SYSTEMS AND METHODS FOR COMPREHENSIVE MULTI-ASSAY TISSUE ANALYSIS 审中-公开
标题翻译：综合多组织分析的系统与方法
公开(公告)号：WO2015052128A1
公开(公告)日：2015-04-16
申请号：PCT/EP2014/071335
申请日：2014-10-06
申请人： VENTANA MEDICAL SYSTEMS, INC. , F. HOFFMANN-LA ROCHE AG
发明人： CHUKKA, Srinivas , SARKAR, Anindya , BREDNO, Joerg
IPC分类号： G06T7/00
CPC分类号： G06K9/0014 , G06K9/00147 , G06T7/0012 , G06T7/0014 , G06T2207/10056 , G06T2207/20104 , G06T2207/30024 , G06T2207/30068 , G06T2207/30072 , G06T2207/30096
摘要： The subject disclosure presents systems and methods for receiving a plurality of assay information along with a query for one or more features of interest, and projecting anatomical information from an anatomical assay onto a staining assay, for example an immunohistochemical (IHC) assay that is commonly registered with the anatomical assay, to locate or determine features appropriate for analysis. The anatomical information may be used to generate a mask that is projected on one or more commonly registered staining assays. A location of the feature of interest in the staining assay may be correlated with the anatomical context provided by the mask, with any features of interest that match the anatomical mask being selected or indicated as appropriate for analysis.
摘要翻译：主题公开内容提供用于接收多个测定信息以及对感兴趣的一个或多个特征的查询的系统和方法，以及将解剖学信息从解剖测定投影到染色测定法上，例如通常的免疫组织化学（IHC）测定注册解剖学测定，以定位或确定适合分析的特征。解剖信息可以用于产生投影在一个或多个通常注册的染色测定中的掩模。染色测定中感兴趣特征的位置可以与由掩模提供的解剖学上下文相关联，其中任何与解剖掩模匹配的特征与选择或指示适合于分析。

3. 发明申请

WO2019110583A1 DEEP-LEARNING SYSTEMS AND METHODS FOR JOINT CELL AND REGION CLASSIFICATION IN BIOLOGICAL IMAGES 审中-公开
公开(公告)号：WO2019110583A1
公开(公告)日：2019-06-13
申请号：PCT/EP2018/083473
申请日：2018-12-04
申请人： VENTANA MEDICAL SYSTEMS, INC. , F. HOFFMANN-LA ROCHE AG
发明人： CHUKKA, Srinivas , SARKAR, Anindya , TAGELDIN, Mohamed, Amgad
IPC分类号： G06K9/00 , G06K9/62
CPC分类号： G06K9/00147 , G06K9/6256
摘要： The present disclosure relates to automated systems and methods for training a multilayer neural network to jointly and simultaneously classify cells and regions from a set of training images. The present disclosure also relates to automated systems and methods for using a trained multilayer neural network to classify cells within an unlabeled image.

4. 发明申请

WO2019025515A1 SYSTEM AND METHOD FOR COLOR DECONVOLUTION OF A SLIDE IMAGE TO ASSIST IN THE ANALYSIS OF TISSUE SPECIMEN 审中-公开
公开(公告)号：WO2019025515A1
公开(公告)日：2019-02-07
申请号：PCT/EP2018/070924
申请日：2018-08-02
申请人： VENTANA MEDICAL SYSTEMS, INC. , F. HOFFMANN-LA ROCHE AG
发明人： CHUKKA, Srinivas , LAN, Zhou
IPC分类号： G06T7/90 , G06T5/00 , G01N21/00
摘要： A tissue analysis system and method for the spectral deconvolution of a RGB digital image obtained from a stained biological tissue sample, by estimating the stain component images that are obtained from a staining system configuration, where the reference stain vectors are assumed to be sampled from a known color distribution. The prior knowledge of stain variability of the staining system is adopted as initial reference stain vectors and statistical distribution of their variability. Based on the initial reference stain vectors distribution, the tissue analysis system determines both the reference stain vectors and stain component images of the input image. The image is then deconvoluted based on the reference stain vectors and stain component images.

5. 发明申请

WO2016108933A1 VESSEL ANALYSIS IN MULTIPLEXED IMAGES 审中-公开
标题翻译：多重图像中的船舶分析
公开(公告)号：WO2016108933A1
公开(公告)日：2016-07-07
申请号：PCT/US2015/000331
申请日：2015-12-24
申请人： VENTANA MEDICAL SYSTEMS, INC. , HOFFMANN-LA ROCHE, INC. , F. HOFFMANN-LA ROCHE AG
发明人： YUAN, Quan , FUCHS, Angelika , CHUKKA, Srinivas , FERNANDEZ, Gerardo , LITTLE, Elizabeth
IPC分类号： G06T7/00
CPC分类号： G06T7/0012 , G06T2207/10056 , G06T2207/20168 , G06T2207/30101
摘要： Systems (100) and methods for analyzing vessels in multiplexed images (101) include detecting large vessels using a spoke feature detection method (113), detecting long and narrow vessels using a line feature detection method (114), detecting smaller vessels using rolling-ball filtering and binary image operations to generate a mask (115), and evaluating any contour polygons resulting from these operations using quality measurements and other thresholds. Maturity determination (117) and nuclei detection (118) are also performed, resulting in an output of vessel characteristics and co-locations enabling enhanced analysis of multispectral images.
摘要翻译：用于分析多路复用图像（101）中的血管的系统（100）和方法包括使用轮辐特征检测方法（113）检测大血管，使用线特征检测方法（114）检测长而窄的血管，球过滤和二进制图像操作以生成掩模（115），并且使用质量测量和其他阈值评估由这些操作产生的任何轮廓多边形。还执行成熟度测定（117）和核子检测（118），导致血管特征和共同位置的输出，从而能够增强对多光谱图像的分析。

6. 发明申请

WO2015113895A1 ADAPTIVE CLASSIFICATION FOR WHOLE SLIDE TISSUE SEGMENTATION 审中-公开
标题翻译：用于全滑动组织分类的自适应分类
公开(公告)号：WO2015113895A1
公开(公告)日：2015-08-06
申请号：PCT/EP2015/051302
申请日：2015-01-23
申请人： VENTANA MEDICAL SYSTEMS, INC. , F. HOFFMANN-LA ROCHE AG
发明人： BREDNO, Joerg , CHUKKA, Srinivas , CHEN, Ting , CHEFD'HOTEL, Christophe , NGUYEN, Kien
IPC分类号： G06K9/00 , G06K9/46 , G06K9/62 , G06K9/66
CPC分类号： G06K9/00147 , G06K9/4642 , G06K9/4652 , G06K9/623 , G06K9/66 , G06T7/0012 , G06T7/11 , G06T7/40 , G06T7/74 , G06T2207/20081 , G06T2207/30024
摘要： A method of segmenting images of biological specimens using adaptive classification to segment a biological specimen into different types of tissue regions. The segmentation is performed by, first, extracting features from the neighborhood of a grid of points (GPs) sampled on the WS image and classifying them into different tissue types. Secondly, an adaptive classification procedure is performed where some or all of the GPs in a WS image are classified using a pre-built training database, and classification confidence scores for the GPs are generated. The classified GPs with high confidence scores are utilized to generate an adaptive training database, which is then used to re-classify the low confidence GPs. The motivation of the method is that the strong variation of tissue appearance makes the classification problem more challenging, while good classification results are obtained when the training and test data origin from the same slide.
摘要翻译：使用自适应分类分割生物样本的图像以将生物样本分割成不同类型的组织区域的方法。分割是通过以下步骤执行的：首先从在WS图像上采样的点（GP）的邻域提取特征并将其分类成不同的组织类型。其次，使用预先构建的训练数据库对WS图像中的一些或全部GP进行分类，并且生成GP的分类置信度得分，进行适应性分类处理。具有高置信度分数的分类GP用于生成适应性训练数据库，然后用于对低置信度GP进行重新分类。该方法的动机是组织外观的强烈变化使得分类问题更具挑战性，而当训练和测试数据来自同一幻灯片时，可以获得良好的分类结果。

7. 发明申请

WO2015049233A1 LINE-BASED IMAGE REGISTRATION AND CROSS-IMAGE ANNOTATION DEVICES, SYSTEMS AND METHODS 审中-公开
标题翻译：基于线的图像注册和交叉映像设备，系统和方法
公开(公告)号：WO2015049233A1
公开(公告)日：2015-04-09
申请号：PCT/EP2014/070927
申请日：2014-09-30
申请人： VENTANA MEDICAL SYSTEMS, INC. , F. HOFFMANN-LA ROCHE AG
发明人： CHUKKA, Srinivas , SARKAR, Anindya , YUAN, Quan
IPC分类号： G06T7/00
CPC分类号： G06T7/0028 , G06F19/00 , G06F19/321 , G06K9/0014 , G06T3/0068 , G06T7/33 , G06T2207/10024 , G06T2207/10056 , G06T2207/10064 , G06T2207/20016 , 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. 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 15 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转移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个的处理。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15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。

8. 发明申请

WO2013113707A1 SYSTEM FOR DETECTING GENES IN TISSUE SAMPLES 审中-公开
标题翻译：用于检测组织样本中的基因的系统
公开(公告)号：WO2013113707A1
公开(公告)日：2013-08-08
申请号：PCT/EP2013/051705
申请日：2013-01-29
申请人： VENTANA MEDICAL SYSTEMS, INC. , F. HOFFMANN-LA ROCHE AG , BAMFORD, Pascal , CHUKKA, Srinivas , MARTIN, Jim F. , SARKAR, Anindya , SERTEL, Olcay , SUZUE, Ellen , VARANGAONKAR, Harshal
发明人： BAMFORD, Pascal , CHUKKA, Srinivas , MARTIN, Jim F. , SARKAR, Anindya , SERTEL, Olcay , SUZUE, Ellen , VARANGAONKAR, Harshal
IPC分类号： G06K9/00 , G06T7/00 , G06T7/40 , C12Q1/68
CPC分类号： G06K9/00147 , C12Q1/6813 , G06T7/0012 , G06T7/90 , G06T2207/10024 , G06T2207/10056 , G06T2207/30024
摘要： A computer-based specimen analyzer (10) is configured to detect a level of expression of genes in a cell sample by detecting dots that represent differently stained genes and chromosomes in a cell. The color of the stained genes and the chromosomes is enhanced and filtered to produce a dot mask that defines areas in the image that are genes, chromosomes, or non-genetic material. Metrics are determined for the dots and/or pixels in the image of the cell in areas corresponding to the dots. The metrics are fed to a classifier that separates genes from chromosomes. The results of the classifier are counted to estimate the expression level of genes in the tissue samples.
摘要翻译：基于计算机的样本分析器（10）被配置为通过检测代表细胞中不同染色的基因和染色体的点来检测细胞样品中基因的表达水平。染色基因和染色体的颜色被增强和过滤以产生定义图像中基因，染色体或非遗传物质区域的点掩模。针对与点对应的区域中的单元格的图像中的点和/或像素确定度量。将度量提供给将染色体分离基因的分类器。计算分类器的结果以估计组织样品中基因的表达水平。

9. 发明申请

WO2019020556A1 METHODS AND SYSTEMS FOR EVALUATION OF IMMUNE CELL INFILTRATE IN TUMOR SAMPLES 审中-公开
公开(公告)号：WO2019020556A1
公开(公告)日：2019-01-31
申请号：PCT/EP2018/069896
申请日：2018-07-23
申请人： VENTANA MEDICAL SYSTEMS, INC. , F. HOFFMANN-LA ROCHE AG
发明人： BARNES, Michael , BREDNO, Joerg , CHUKKA, Srinivas , SHANMUGAM, Kandavel , BOWERMASTER, Rebecca C. , LIU, Wen-Wei , ZHU, Junming
IPC分类号： G06T7/00 , C12Q1/6886 , G01N33/574
摘要： Immune context scores are calculated for tumor tissue samples using continuous scoring functions. Feature metrics for at least one immune cell marker are calculated for a region or regions of interest, the feature metrics including at least a quantitative measure of human CD3 or total lymphocyte counts. A continuous scoring function is then applied to a feature vector including the feature metric and at least one additional metric related to an immunological biomarker, the output of which is an immune context score. The immune context score may then be plotted as a function of a diagnostic or treatment metric, such as a prognostic metric (e.g. overall survival, disease-specific survival, progression-free survival) or a predictive metric (e.g. likelihood of response to a particular treatment course). The immune context score may then be incorporated into diagnostic and/or treatment decisions.

10. 发明申请

WO2018091486A1 CONVOLUTIONAL NEURAL NETWORKS FOR LOCATING OBJECTS OF INTEREST IN IMAGES OF BIOLOGICAL SAMPLES 审中-公开
公开(公告)号：WO2018091486A1
公开(公告)日：2018-05-24
申请号：PCT/EP2017/079246
申请日：2017-11-15
申请人： VENTANA MEDICAL SYSTEMS, INC. , F. HOFFMANN-LA ROCHE AG
发明人： CHUKKA, Srinivas , CHEN, Jianxu
IPC分类号： G06K9/00 , G06N3/02 , G06K9/46 , G06K9/62
摘要： Convolutional neural networks for detecting objects of interest within images of biological specimens are disclosed. Also disclosed are systems and methods of training and using such networks, one method including: obtaining a sample image and at least one of a set of positive points and a set of negative points, wherein each positive point identifies a location of one object of interest within the sample image, and each negative point identifies a location of one object of no-interest within the sample image; obtaining one or more predefined characteristics of objects of interest and/or objects of no-interest, and based on the predefined characteristics, generating a boundary map comprising a positive area around each positive point the set of positive points, and/or a negative area around each negative point in the set of negative points; and training the convolutional neural network using the sample image and the boundary map.

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