Ventana Medical Systems, Inc.

The Cleveland Clinic Foundation

The University of Melbourne

Anindya Sarkar

Srinivas Chukka

Jim F Martin

Joerg Bredno

Quan Yuan

Olcay Sertel

Michael S Barnes

Bikash Sabata

Pascal Bamford

Ellen Suzue

Jeffrey Atchison

Chibuya Siame

Ting Chen

Thomas M Grogan

Harshal Varangaonkar

Ronald T Kurnik

Christophe Chefd'hotel

Kien Nguyen

Shalini Singh

Suhas Hanmantrao Patil

Lou Dietz

Auranuch Lorsakul

Sujit Siddheshwar Chivate

Christoph Guetter

Crystal Schemp

Raymond Tubbs

Paul Waring

Mohamed Amgad Tageldin

Nikolaus Wick

Paul Michael Waring

Techniques for acquiring focused images of a microscope slide are disclosed. During a calibration phase, a 'base' focal plane is determined using non-synthetic and/or synthetic auto-focus techniques. Furthermore, offset planes are determined for color channels (or filter bands) and used to generate an auto-focus model. During subsequent scans, the auto-focus model can be used to quickly estimate the focal plane of interest for each color channel (or filter band) rather than re-employing the non-synthetic and/or synthetic auto-focus techniques.

Auto-focus methods and systems for multi-spectral imaging

The present disclosure provides for image processing systems and methods for analyzing digital images of biological samples stained for the presence of protein and/or nucleic acid biomarkers and detecting and quantifying signals corresponding to one or more biomarkers. The present disclosure also provides systems and methods for the clinical interpretation of dual ISH slides where the cells to score are selected (e.g. by using one or more cell detection and identification algorithms. By detecting, identifying, and selecting cells for assessment, it is believed that subjectivity is reduced or eliminated. The systems and methods also allow for an increased number of cells to be considered for scoring as compared with manual dot counting methods, thereby increasing detection sensitivity, ultimately enabling improved patient care and treatment.

Systems for automated in situ hybridization analysis

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.

Line-based image registration and cross-image annotation devices, systems and methods

A computer-based specimen analyzer () 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.

System and method for detecting gene expression levels in tissue samples

A method and system for measuring the alignment between a substrate and a platform upon which it is disposed by using image processing algorithms are described herein. These algorithms automate the detection of edges of a microscope slide and the platform in a digital image. A reference line pattern in an image of the platform can be used to detect platform edges based on a computed location of the reference line pattern in the image.

Method and system to detect substrate placement accuracy

The present disclosure provides for image processing systems and methods for automatically analyzing digital images of biological samples stained for the presence of protein and/or nucleic acid biomarkers () and automatically detecting and quantifying signals corresponding to one or more biomarkers (). The present disclosure also provides systems and methods for the clinical interpretation of dual ISH slides where the cells to score are automatically selected (e.g. by using one or more cell detection and identification algorithms ()). By automatically detecting, identifying, and selecting cells for assessment, it is believed that subjectivity is reduced or eliminated. The automated systems and methods also allow for an increased number of cells to be considered for scoring as compared with manual dot counting methods, thereby in-creasing detection sensitivity, ultimately enabling improved patient care and treatment.

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.

Quality metrics for automatic evaluation of dual ISH images

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&#x2014;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&#x2014;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.

The present disclosure describes a method of foreground segmentation and nucleus ranking for scoring dual ISH images. The method has been developed to better identify those nuclei, within a selected field of view, that meet the criteria for dual ISH scoring.

Foreground segmentation and nucleus ranking for scoring dual ISH images

Techniques are provided for stitching tile images. A local registration can be performed to determine spatial relationships between pairs of adjacent tile images, in which the spatial relationships may be determined using normalized cross correlation (NCC) scores computed within a multi-resolution framework. A global placement can be performed to position all scanned tile images relative to one another. The global placement is determined with weighted least squares utilizing the determined spatial relationships between all adjacent greyscale tile images and the NCC scores as weights.

Local tile-based registration and global placement for stitching

Automated systems and methods for training a multilayer neural network to classify cells and regions from a set of training images are presented. Automated systems and methods for using a trained multilayer neural network to classify cells within an unlabeled image are also presented.

Deep-learning systems and methods for joint cell and region classification in biological images

Systems and methods for automatically excluding artifacts from an analysis of a biological specimen image are disclosed. An exemplary method includes obtaining an immunohistochemistry (IHC) image and a control image, determining whether the control image includes one or more artifacts, upon a determination that the control image includes one or more artifacts, identifying one or more artifact regions within the IHC image by mapping the one or more artifacts from the control image to the IHC image, and performing image analysis of the IHC image where any identified artifact regions are excluded from the image analysis.

Image analysis algorithms using control slides

Automated systems and methods for determining the variability between derived expression scores for a series of biomarkers between different identified cell clusters in a whole slide image are presented. The variability between derived expression scores may be a derived inter-marker heterogeneity metric.

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Milpitas, CA, United States of America

Anindya Sarkar