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1. 发明申请

US20220189016A1 ASSESSING RISK OF BREAST CANCER RECURRENCE 有权
公开(公告)号：US20220189016A1
公开(公告)日：2022-06-16
申请号：US17685115
申请日：2022-03-02
申请人： Ventana Medical Systems Inc. , The Board of Trustees of the Leland Stanford Junior University
发明人： Michael Barnes , Srinivas Chukka , David Knowles
IPC分类号： G06T7/00 , C12Q1/6886 , G06K9/62 , G06V20/69
摘要： The subject disclosure presents systems and computer-implemented methods for assessing a risk of cancer recurrence in a patient based on a holistic integration of large amounts of prognostic information for said patient into a single comparative prognostic dataset. A risk classification system may be trained using the large amounts of information from a cohort of training slides from several patients, along with survival data for said patients. For example, a machine-learning-based binary classifier in the risk classification system may be trained using a set of granular image features computed from a plurality of slides corresponding to several cancer patients whose survival information is known and input into the system. The trained classifier may be used to classify image features from one or more test patients into a low-risk or high-risk group.

2. 发明授权

US11257209B2 Cancer risk stratification based on histopathological tissue slide analysis 有权
公开(公告)号：US11257209B2
公开(公告)日：2022-02-22
申请号：US15613113
申请日：2017-06-02
申请人： Ventana Medical Systems, Inc.
发明人： Michael Barnes , Srinivas Chukka , Bonnie LaFleur , Chang Xu
IPC分类号： G06T7/00 , G16H50/30 , G06K9/62 , G06K9/00 , G06T7/12 , G01N33/574 , G06T7/11
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.

3. 发明授权

US11113809B2 Group sparsity model for image unmixing 有权
公开(公告)号：US11113809B2
公开(公告)日：2021-09-07
申请号：US16590942
申请日：2019-10-02
申请人： Ventana Medical Systems, Inc.
发明人： Srinivas Chukka , Ting Chen
IPC分类号： G06K9/00 , G06T7/00 , G06T7/90
摘要： Systems and methods described herein relate, among other things, 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.

4. 发明授权

US10977766B2 Line-based image registration and cross-image annotation devices, systems and methods 有权
公开(公告)号：US10977766B2
公开(公告)日：2021-04-13
申请号：US16706630
申请日：2019-12-06
申请人： Ventana Medical Systems, Inc.
发明人： Srinivas Chukka , Anindya Sarkar , Quan Yuan
IPC分类号： G06T7/33 , G06T3/00 , G06K9/00 , G16H30/20 , G16H30/40
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.

5. 发明授权

US10521644B2 Lab color space silver and red in situ hybridization based techniques for detecting genes in tissue samples 有权
公开(公告)号：US10521644B2
公开(公告)日：2019-12-31
申请号：US14372673
申请日：2013-01-29
申请人： Ventana Medical Systems, Inc.
发明人： Pascal Bamford , Srinivas Chukka , Jim F. Martin , Anindya Sarkar , Olcay Sertel , Ellen Suzue , Harshal Varangaonkar
IPC分类号： G06K9/00 , G06T7/00 , C12Q1/6813 , G06T7/90 , G06K9/46
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.

6. 发明授权

US10445619B2 Automatic glandular and tubule detection in histological grading of breast cancer 有权
公开(公告)号：US10445619B2
公开(公告)日：2019-10-15
申请号：US15418632
申请日：2017-01-27
申请人： Ventana Medical Systems, Inc.
发明人： Michael Barnes , Christophe Chefd'hotel , Srinivas Chukka , Kien Nguyen
IPC分类号： G06K9/62 , G06K9/00 , G06T7/11 , G06T7/13 , G06K9/46 , G06K9/66 , G06T7/00 , G06T7/40
摘要： Methods, systems, and apparatuses for automatically identifying glandular regions and tubule regions in a breast tissue sample are provided. An image of breast tissue is analyzed to detect nuclei and lumen candidates, identify tumor nuclei and true lumen from the candidates, and group tumor nuclei with neighboring tumor nuclei and lumina to define tubule glandular regions and non-tubule glandular regions of the image. Learnt supervised classifiers, such as random forest classifiers, can be applied to identify and classify the tumor nuclei and true lumina. Graph-cut methods can be applied to group the tumor nuclei and lumina and to define the tubule glandular regions and non-tubule glandular regions. The analysis can be applied to whole slide images and can resolve tubule areas with multiple layers of nuclei.

7. 发明授权

US10430945B2 Systems and methods for color deconvolution 有权
公开(公告)号：US10430945B2
公开(公告)日：2019-10-01
申请号：US15422343
申请日：2017-02-01
申请人： Ventana Medical Systems, Inc.
发明人： Ting Chen , Srinivas Chukka
IPC分类号： G06T7/00 , G06T7/90
摘要： The present disclosure involves a computer-implemented unmixing algorithm that employs a least-square method involving image patches, and a computer-implemented unmixing or color deconvolution method that incorporates spatial smoothness and structure continuity constraints, for example, into a neighborhood graph regularizer, such as using a graph to enforce the pixel value similarities for those pixels in the same neighborhood.

8. 发明授权

US10388014B2 Vessel analysis in multiplexed images 有权
公开(公告)号：US10388014B2
公开(公告)日：2019-08-20
申请号：US15633945
申请日：2017-06-27
申请人： Ventana Medical Systems, Inc. , Hoffmann-La Roche, Inc.
发明人： Quan Yuan , Srinivas Chukka , Gerardo Fernandez , Elizabeth Little , Angelika Fuchs
IPC分类号： G06T7/00
摘要： Systems and methods for analyzing vessels in multiplexed images include detecting large vessels using a spoke feature detection method, detecting long and narrow vessels using a line feature detection method, detecting smaller vessels using rolling-ball filtering and binary image operations to generate a mask, and evaluating any contour polygons resulting from these operations using quality measurements and other thresholds. Maturity determination and nuclei detection are also performed, resulting in an output of vessel characteristics and co-locations enabling enhanced analysis of multispectral images.

9. 发明授权

US10176579B2 Tissue object-based machine learning system for automated scoring of digital whole slides 有权
公开(公告)号：US10176579B2
公开(公告)日：2019-01-08
申请号：US14776571
申请日：2014-03-12
申请人： Ventana Medical Systems, Inc.
发明人： Srinivas Chukka , Sujit Siddheshwar Chivate , Suhas Hanmantrao Patil , Bikash Sabata , Olcay Sertel , Anindya Sarkar
IPC分类号： G06T7/00 , G06K9/00 , G06T7/11
摘要： A facility includes systems and methods for providing a learning-based image analysis approach for the automated detection, classification, and counting of objects (e.g., cell nuclei) within digitized pathology tissue slides. The facility trains an object classifier using a plurality of reference sample slides. Subsequently, and in response to receiving a scanned image of a slide containing tissue data, the facility separates the whole slide into a background region and a tissue region using image segmentation techniques. The facility identifies dominant color regions within the tissue data and identifies seed points within those regions using, for example, a radial symmetry based approach. Based at least in part on those seed points, the facility generates a tessellation, each distinct area in the tessellation corresponding to a distinct detected object. These objects are then classified using the previously-trained classifier. The facility uses the classified objects to score slides.

10. 发明申请

US20170140246A1 AUTOMATIC GLANDULAR AND TUBULE DETECTION IN HISTOLOGICAL GRADING OF BREAST CANCER 审中-公开
公开(公告)号：US20170140246A1
公开(公告)日：2017-05-18
申请号：US15418632
申请日：2017-01-27
申请人： Ventana Medical Systems, Inc.
发明人： Michael Barnes , Christophe Chefd'hotel , Srinivas Chukka , Kien Nguyen
IPC分类号： G06K9/62 , G06T7/11 , G06K9/66 , G06T7/40 , G06K9/46 , G06T7/00 , G06T7/13
CPC分类号： G06K9/6227 , G06K9/00127 , G06K9/0014 , G06K9/00147 , G06K9/4647 , G06K9/6256 , G06K9/66 , G06T7/0012 , G06T7/11 , G06T7/13 , G06T7/40 , G06T2207/30024 , G06T2207/30068 , G06T2207/30096
摘要： Methods, systems, and apparatuses for automatically identifying glandular regions and tubule regions in a breast tissue sample are provided. An image of breast tissue is analyzed to detect nuclei and lumen candidates, identify tumor nuclei and true lumen from the candidates, and group tumor nuclei with neighboring tumor nuclei and lumina to define tubule glandular regions and non-tubule glandular regions of the image. Learnt supervised classifiers, such as random forest classifiers, can be applied to identify and classify the tumor nuclei and true lumina. Graph-cut methods can be applied to group the tumor nuclei and lumina and to define the tubule glandular regions and non-tubule glandular regions. The analysis can be applied to whole slide images and can resolve tubule areas with multiple layers of nuclei.

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