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1. 发明申请

WO2022036086A1 MACHINE LEARNING MODELS FOR CELL LOCALIZATION AND CLASSIFICATION LEARNED USING REPEL CODING 审中-公开
公开(公告)号：WO2022036086A1
公开(公告)日：2022-02-17
申请号：PCT/US2021/045728
申请日：2021-08-12
申请人： VENTANA MEDICAL SYSTEMS, INC.
发明人： MARTIN, Jim F. , MUKHERJEE, Satarupa , NIE, Yao
IPC分类号： G06K9/00 , G06K9/46 , G06T7/00
摘要： The present disclosure relates to computer-implement techniques for cell localization and classification. Particularly, aspects of the present disclosure are directed to accessing an image for a biological sample, where the image depicts cells comprising a staining pattern of a biomarker; inputting the image into a machine learning model; encoding, by the machine learning model, the image into a feature representation comprising extracted discriminative features; combining, by the machine learning model, feature and spatial information of the cells and the staining pattern of the biomarker through a sequence of up-convolutions and concatenations with the extracted discriminative features from the feature representation; and generating, by the machine learning model, two or more segmentation masks for the biomarker in the image based on the combined feature and spatial information of the cells and the staining pattern of the biomarker.

2. 发明申请

WO2023043709A1 MACHINE-LEARNING TECHNIQUES FOR PREDICTING PHENOTYPES IN DUPLEX DIGITAL PATHOLOGY IMAGES 审中-公开
公开(公告)号：WO2023043709A1
公开(公告)日：2023-03-23
申请号：PCT/US2022/043285
申请日：2022-09-13
申请人： VENTANA MEDICAL SYSTEMS, INC.
发明人： MARTIN, Jim F. , NIE, Yao , VENUGOPAL, Raghavan , WANG, Xingwei
IPC分类号： G06V20/69 , G06V10/80 , G06V10/82
摘要： Duplex immunohistochemistry (IHC) staining of tissue sections allows simultaneous detection of two biomarkers and their co-expression at the single-cell level, and does not require two IHC stains and additional registration to identify co-localization. Duplex IHC are often difficult for human including pathologists to reliably score. The methods and system herein use machine-learning models and probability maps to detect and record individual phenotype ER/PR.

3. 发明申请

WO2016120441A2 QUALITY METRICS FOR AUTOMATIC EVALUATION OF DUAL ISH IMAGES 审中-公开
标题翻译：用于自动评估双重ISH图像的质量度量
公开(公告)号：WO2016120441A2
公开(公告)日：2016-08-04
申请号：PCT/EP2016/051903
申请日：2016-01-29
申请人： VENTANA MEDICAL SYSTEMS, INC. , F. HOFFMANN-LA ROCHE AG
发明人： MARTIN, Jim F. , SARKAR, Anindya , ATCHISON, Jeffrey
IPC分类号： G06T7/00 , G06T5/00 , G06T5/50
CPC分类号： G06T5/003 , G02B21/367 , G06T5/50 , G06T7/0002 , G06T2207/10024 , G06T2207/10056 , G06T2207/10148 , G06T2207/30024 , G06T2207/30168
摘要： The present disclosure is directed to a computer system designed to (i) receive a series of images as input; (ii) compute a number of metrics derived from focus features and color separation features within the images; and (iii) evaluate the metrics to return (a) an identification of the most suitable z-layer in a z-stack, given a series of z-layer images in a z-stack; and/or (b) an identification of those image tiles that are more suitable for cellular based scoring by a medical professional, given a series of image tiles from an area of interest of a whole slide scan.
摘要翻译：本公开针对被设计为（i）接收一系列图像作为输入的计算机系统; （ii）计算从图像内的焦点特征和颜色分离特征导出的度量数; 并且（iii）评估度量以给出z叠层中的一系列z层图像以返回z叠层中最合适z层的标识; 和/或（b）给定从整个幻灯片扫描的感兴趣区域的一系列图像瓦片的那些更适合于由医疗专业人员进行基于蜂窝的评分的图像瓦片的识别。

4. 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。

5. 发明申请

WO2021224293A1 HISTOCHEMICAL SYSTEMS AND METHODS FOR EVALUATING EGFR AND EGFR LIGAND EXPRESSION IN TUMOR SAMPLES 审中-公开
公开(公告)号：WO2021224293A1
公开(公告)日：2021-11-11
申请号：PCT/EP2021/061778
申请日：2021-05-05
申请人： VENTANA MEDICAL SYSTEMS, INC. , F. HOFFMANN-LA ROCHE AG
发明人： BARNES, Michael , BREDNO, Joerg , KELLY, Brian D. , MARTIN, Jim F. , MURANYI, Andrea , PINEDA, Carlos T. , SHANMUGAM, Kandavel
IPC分类号： G01N33/574 , G01N33/74 , G06T7/00 , G16H30/00
摘要： Methods and systems for predictive measures of anti-EGFR therapy response in wild type RAS/EGFR+ samples, e.g., histochemical staining methods for staining EGFR, AREG, and EREG, digital analysis of stained slides, and scoring algorithms that allow prediction of a response to anti-EGFR therapies. Analysis of the stained slides and scoring algorithms may include but are not limited to: a percent tumor cell positivity, computerized clustering algorithms, area density (e.g., area of tumor positive for one or more markers over total tumor area), average intensity (e.g., computerized methodology measuring average gray scale pixel intensity), average intensity broken down according to membrane, cytoplasmic, or punctate staining patterns), or any other appropriate parameter or combination of parameters. The methods of the present invention allow for resolving spatial expression patterns of the ligands and the receptor to determine what patterns are predictive for response to anti-EGFR therapies.

6. 发明申请

WO2016120463A1 SYSTEMS AND METHODS FOR AREA-OF-INTEREST DETECTION USING SLIDE THUMBNAIL IMAGES 审中-公开
标题翻译：使用幻灯片图像进行区域感知检测的系统和方法
公开(公告)号：WO2016120463A1
公开(公告)日：2016-08-04
申请号：PCT/EP2016/051967
申请日：2016-01-29
申请人： VENTANA MEDICAL SYSTEMS, INC. , F. HOFFMANN-LA ROCHE AG
发明人： MARTIN, Jim F. , SARKAR, Anindya
IPC分类号： G06T7/00 , G06K9/00
CPC分类号： G06T7/0012 , G06F3/0482 , G06K9/00127 , G06T7/11 , G06T2200/24 , G06T2207/10056 , G06T2207/20092 , G06T2207/20132 , G06T2207/30024 , G06T2207/30072
摘要： The subject disclosure provides systems and methods for determination of Area of Interest (AOI) for different types of input slides. Slide thumbnails may be assigned into one of five different types, and separate algorithms for AOI detection executed depending on the slide type. Slide types include ThinPrep (RTM) slides, tissue micro-array (TMA) slides, control HER2 slides with 4 cores, smear slides, and a generic slide. The slide type may be assigned based on a user input. Customized AOI detection operations are provided for each slide type. If the user enters an incorrect slide type, operations include detecting the incorrect input and executing the appropriate method. The result of each AOI detection operations provides as its output a soft-weighted image having zero intensity values at pixels that are detected as not belonging to tissue, and higher intensity values assigned to pixels detected as likely belonging to tissue regions.
摘要翻译：主题公开提供了用于确定不同类型输入幻灯片的兴趣区域（AOI）的系统和方法。滑动缩略图可以被分配为五种不同类型之一，并且根据幻灯片类型执行AOI检测的单独算法。幻灯片类型包括ThinPrep（RTM）幻灯片，组织微阵列（TMA）幻灯片，具有4个核心的对照HER2幻灯片，涂片幻灯片和通用幻灯片。可以基于用户输入来分配幻灯片类型。为每种幻灯片类型提供定制的AOI检测操作。如果用户输入错误的幻灯片类型，操作包括检测不正确的输入并执行适当的方法。每个AOI检测操作的结果提供了在被检测为不属于组织的像素处具有零强度值的软加权图像，以及分配给可能属于组织区域的像素分配的较高强度值。

7. 发明申请

WO2016120440A1 DOT DETECTION, COLOR CLASSIFICATION OF DOTS AND COUNTING OF COLOR CLASSIFIED DOTS 审中-公开
标题翻译： DOOT检测，DOTS的颜色分类和颜色分类DOTS的计数
公开(公告)号：WO2016120440A1
公开(公告)日：2016-08-04
申请号：PCT/EP2016/051902
申请日：2016-01-29
申请人： VENTANA MEDICAL SYSTEMS, INC. , F. HOFFMANN-LA ROCHE AG
发明人： MARTIN, Jim F. , SARKAR, Anindya
IPC分类号： G06T7/00 , G06T7/40 , G06K9/00
CPC分类号： G06K9/00127 , G06T7/0012 , G06T7/11 , G06T7/90 , G06T2207/10024 , G06T2207/10056 , G06T2207/30024 , G06T2207/30096
摘要： The present disclosures relates to a method of detecting, classifying, and counting dots in an image of a tissue specimen comprising detecting dots in an image of the tissue sample that meet criteria for absorbance strength, black unmixed image channel strength, red unmixed image channel strength, and a difference of Gaussian threshold, wherein the detected dots correspond to in situ hybridization signals in the tissue samples (320); classifying the detected dots as belonging to a black in situ hybridization signal or to a red in situ hybridization signal (330); and calculating a ratio of those dots belonging to the black in situ hybridization signal and those belonging to the red in situ hybridization signal (350).
摘要翻译：本公开涉及组织样本的图像中的点的检测，分类和计数的方法，包括检测组织样本的图像中满足吸光度强度，黑色未混合图像通道强度，红色未混合图像通道强度的标准的点和高斯阈值的差异，其中所检测的点对应于组织样本（320）中的原位杂交信号; 将检测到的点分类为属于黑原位杂交信号或红色原位杂交信号（330）; 并计算属于黑原位杂交信号的那些点和属于红原位杂交信号的那些点的比例（350）。

8. 发明申请

WO2023064186A1 MACHINE-LEARNING TECHNIQUES FOR DETECTING ARTIFACT PIXELS IN IMAGES 审中-公开
公开(公告)号：WO2023064186A1
公开(公告)日：2023-04-20
申请号：PCT/US2022/046096
申请日：2022-10-07
申请人： VENTANA MEDICAL SYSTEMS, INC.
发明人： BA, Qinle , MARTIN, Jim F. , ZUIDERVELD, Karel J. , HORCHNER, Uwe
IPC分类号： G06V10/82 , G06V20/69
摘要： Method and systems for of using a machine-learning model to detect predicted artifacts at a target image resolution are provided. A machine-learning model trained to detect artifact pixels in images at a target image resolution is accessed. An image depicting at least part of the biological sample at an initial image resolution can be converted at the target image resolution. The machine-learning model is applied to the converted image to identify one or more artifact pixels from the converted image. Method and systems for training the machine-learning model to detect predicted artifacts at the target image resolution are also provided.

9. 发明申请

WO2021163213A1 FEDERATED LEARNING SYSTEM FOR TRAINING MACHINE LEARNING ALGORITHMS AND MAINTAINING PATIENT PRIVACY 审中-公开
公开(公告)号：WO2021163213A1
公开(公告)日：2021-08-19
申请号：PCT/US2021/017491
申请日：2021-02-10
申请人： VENTANA MEDICAL SYSTEMS, INC.
发明人： AGHAEI, Faranak , MURARI, Nidhin , MARTIN, Jim F. , SCHMID, Joachim , SHEIKHZADEH, Fahime , SOM, Anirudh
IPC分类号： G06K9/00
摘要： A method for using a federated learning classifier in digital pathology includes distributing, by a centralized server, a global model to a plurality of client devices. The client devices further train the global model using a plurality images of a specimen and corresponding annotations to generate at least one further trained model. The client devices provide further trained models to the centralized server, which aggregates the further trained models with the global model to generate an updated global model. The updated global model is then distributed to the plurality of client devices.

10. 发明申请

WO2021086671A1 IMAGE-PROCESSING-BASED OBJECT CLASSIFICATION USING BOOTSTRAPPING OF REGION-LEVEL ANNOTATIONS 审中-公开
公开(公告)号：WO2021086671A1
公开(公告)日：2021-05-06
申请号：PCT/US2020/056340
申请日：2020-10-19
申请人： VENTANA MEDICAL SYSTEMS, INC.
发明人： TAGELDIN, Mohamed Amgad , COOPER, Lee Alex Donald , KURKURE, Uday , MARTIN, Jim F.
IPC分类号： G06K9/00 , G06K9/32 , G06K9/46 , G06K9/62
摘要： Techniques relate to object classifications using bootstrapping of region-level annotations. For each of multiple images, regions within the image can be identified. For each region, a region-specific label can be identified, a set of objects within the region can be detected, and an object-specific label can be assigned to each object. The object-specific label can be the same as the region-specific label assigned to the region within which the object is located. A training data set can be defined to include, for each image of the multiple images, object-location data (indicating intra-image location data for the detected object) and label data (indicating the object-specific labels assigned to the objects). An image-processing model can be trained using the training data. Training can include learning values for a set of parameters that define calculations performed by the image-processing model.

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