General Electric Company

Ge Healthcare Bio-Sciences Corp.

Qing Li

Alberto Santamaria-Pang

Fiona Ginty

Christopher James Sevinsky

Anup Sood

Michael John Gerdes

Brion Daryl Sarachan

Yousef Ahmed Al-Kofahi

Raghav Krishna Padmanabhan

Ali Can

Dmitry Vladimirovich Dylov

Musodiq O Bello

Michael Ernest Marino

Zhengyu Pang

Daniel Eugene Meyer

Yunxia Sui

Edward John Moler

Jens Rittscher

Dirk Ryan Padfield

Megan Pearl Rothney

Srabani Bhaumik

Aritra Chowdhury

Keyur Hemantkumar Desai

Nicole Evelyn LaPlante

Fiona Mary Ginty

The present approach relates to the use of trained artificial neural networks, such as convolutional neural networks, to classify vascular structures, such as using a hierarchical classification scheme. In certain approaches, the artificial neural network is trained using training data that is all or partly derived from synthetic vascular representations.

Methods of systems of generating virtual multi-dimensional models using image analysis

The subject matter of the present disclosure generally relates to techniques for image analysis. In certain embodiments, various morphological or intensity-based features as well as different thresholding approaches may be used to segment the subpopulation of interest and classify object in the images.

Systems and methods for automatic generation of training sets for machine interpretation of images

The disclosed subject matter relates to an automated determination of cell-by-cell segmentation quality of a tissue specimen sample. A training set of cells is examined by an expert to determine which cells that include 'good' segmentation and which cells include 'poor' segmentation. A training model is build based on the image data of the cells in the training set. Image data from cells in a test specimen is obtained and that image data is compared to the training model to determine on a cell-by-cell basis which cells in the test specimen include &#x201c;good&#x201d; segmentation and which cells include &#x201c;poor&#x201d; segmentation. The accumulated data on the cells analyzed in the test specimen can be utilized to determine an overall segmentation quality score for the area of the test specimen in which the individual cells are located in the test specimen.

Automated cell segmentation quality control

Disclosed are novel computer-implemented methods for creating a blood vessel map of a biological tissue. The methods comprise the steps of, accessing image data corresponding to multi-channel multiplexed image of a fluorescently stained biological tissue manifesting expression levels of a primary marker and at least one auxiliary marker of blood vasculature, and extracting features of blood vessels using the primary marker as an input to create a single channel segmentation of the blood vessels. The method further comprises the steps of extracting features of blood vessels using the auxiliary marker to create auxiliary channels as a second input and apply multi-channel blood vessel enhancement. Blood vessel maps are created using the features and tracing the blood vasculature by iteratively extending the centerlines of the initial segmentation using statistical models and geometric rules.

System and method for blood vessel analysis and quantification in highly multiplexed fluorescence imaging

The disclosed embodiments are directed to a method for accurately counting and characterizing multiple cell phenotypes and sub-phenotypes within cell populations simultaneously by exploiting biomarker co-expression levels within cells of different phenotypes in the same tissue sample. The disclosed embodiments are also directed to a simple intuitive interface enabling medical staff (e.g., pathologists, biologists) to annotate and evaluate different cell phenotypes used in the algorithm and the presented through the interface.

Method and system for classification and quantitative analysis of cell types in microscopy images

The disclosed subject matter relates to an automated determination the stain quality and segmentation quality of a tissue sample. By way of example, separate image data is acquired of an unstained form of a biological specimen, the biological specimen stained with a nuclei marker and the biological specimen stained with a segmentation marker. A correlation map (Cr) from the separate image data and a ridgeness map (Pr) from the image data of the biological specimen stained with a segmentation marker are each determined. A staining quality score and segmentation quality score are then determined from the correlation map (Cr) and the ridgeness map (Pr).

Automated staining and segmentation quality control

The present disclosure relates to characterization of biological samples. By way of example, a biological sample may be contacted with a plurality of probes specific for targets in the sample, such as probes for immune markers and segmenting probes. Acquired image data of the sample may be used to segment the images into epithelial and stromal regions to characterize individual cells in the sample based on the binding of the probes. Further, the biological sample may be characterized by a distribution, location, and type of a plurality of the characterized cells.

Quantitative in situ characterization of biological samples

Improved systems and methods for the analysis of digital images are provided. More particularly, the present disclosure provides for improved systems and methods for the analysis of digital images of biological tissue samples. Exemplary embodiments provide for: i) segmenting, ii) grouping, and iii) quantifying molecular protein profiles of individual cells in terms of sub cellular compartments (nuclei, membrane, and cytoplasm). The systems and methods of the present disclosure advantageously perform tissue segmentation at the sub-cellular level to facilitate analyzing, grouping and quantifying protein expression profiles of tissue in tissue sections globally and/or locally. Performing local-global tissue analysis and protein quantification advantageously enables correlation of spatial and molecular configuration of cells with molecular information of different types of cancer.

System and method for multiplexed biomarker quantitation using single cell segmentation on sequentially stained tissue

A method for generating a brightfield type image, which resembles a brightfield staining protocol of a biological sample, using fluorescent images is provided. The steps comprise acquiring two or more fluorescent images of a fixed area on a biological sample, mapping said fluorescent image into a brightfield color space, and generating a bright field image. Also provided is an image analysis system for generating a brightfield type image of a biological sample using fluorescent images.

System and methods for mapping fluorescent images into a bright field color space

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Niskayuna, NY, United States of America

Qing Li