General Electric Company

Lockheed Martin Corporation

Exxonmobil Upstream Research Company

Ge Healthcare Bio-Sciences Corp.

Utc Fire & Security Americas Corporation, Inc.

Ge Security, Inc.

Lockheed Martin Company

Exxonmobil Research Upstream

Ge Healthcare Limited

Global Life Sciences Solutions USA LLC

Jens Rittscher

Peter Henry Tu

Thomas Baby Sebastian

GianFranco Doretto

Nils Oliver Krahnstoever

Dirk Ryan Padfield

Ali Can

Alberto Santamaria-Pang

Xiaogang Wang

Xiaoming Liu

Timothy Patrick Kelliher

Musodiq O Bello

Ser Nam Lim

Li Guan

Ting Yu

Rahul Bhotika

Brion Daryl Sarachan

Jilin Tu

David Scott Diwinsky

Jeffrey Wayne Eberhard

Bernhard Erich Hermann Claus

Robert John Filkins

Jeffrey Michael Ashe

Douglas Henry Marman

Yves Lucien Trousset

Matthias Imhof

Kai Erik Thomenius

Joseph Manak

Nicholas Thomas

Hao Lai

Ya Xue

Jie Yu

Stewart Abbot

Paulo Ricardo Dos Santos Mendonca

Cyril Riddell

Leslie A Wahrmund

Paulo Ricardo Mendonca

Dirk Padfield

Ahmad Yekta

Yuchi Huang

Fiona Ginty

Kedar Bhalchandra Khare

Qing Li

Vidya Pundalik Kamath

Zhengyu Pang

John Robert Hare

Christopher James Sevinsky

Shubao Liu

Megan Pearl Rothney

Gianni Matteucci

John Charles Hill, Jr

Weina Ge

Erhan Bas

Xingwei Yang

Xudong Li

Jose Abiel Garza

Raghu Machiraju

Kelly E Wrobel

Bikash Chandra Mahato

Ambalangoda Gurunnanselage Amitha Perera

Harold Woodruff Tomlinson

Kevin Chean

Ying Li

Amitha Perera

Cemal Cagatay Bilgin

Kenneth R Turner

Elizabeth P Roquemore

Umesha Adiga

Kishore Mosaliganti

Antonio Rafael Da Costa Paiva

Stewart E Abbot

Dirk R Padfield

Umesh Adiga

Fiona Mary Ginty

A workflow is presented that facilitates defining geocontextual information as a set of rules for multiple seismic attributes. Modeling algorithms may be employed that facilitate analysis of multiple seismic attributes to find candidate regions that are most likely to satisfy the set of rules. These candidates may then be sorted based on how well they represent the geocontextual information.

Context based geo-seismic object identification

Embodiments of the disclosure are directed to segmenting a digital image of biological tissue into biological units, such as cells. A first weak or data driven segmentation is generated using image data representing the digital image to segment the digital image into a first set of biological units. Applying a geometric model, each unit in the first set of biological units is ranked based on a similarity in shape and scale between the unit and one or more other units in the image. A subset of units from the first set of biological units is selected based on the rank of each biological unit relative to a predetermined threshold rank. A second weak or data driven segmentation may then be generated using image data including the subset of biological units to segment that portion of the digital image into a second set of biological units.

Biological unit segmentation with ranking based on similarity applying a geometric shape and scale model

A method for image based inspection of an object includes receiving an image of an object from an image capture device, wherein the image includes a representation of the object with mil-level precision. The method further includes projecting a measurement feature of the object from the image onto a three-dimensional (3D) model of the object based on a final projection matrix; determining a difference between the projected measurement feature and an existing measurement feature on the 3D model; and sending a notification including the difference between the projected measurement feature and the existing measurement feature.

System and method for image based inspection of an object

An approach for seismic data analysis is provided. In accordance with various embodiments, the active learning approaches are employed in conjunction with an analysis algorithm that is used to process the seismic data. Algorithms that may employ such active learning include, but are not limited to, ranking algorithms and classification algorithms.

Seismic data analysis

In accordance with one aspect of the present technique, a method is disclosed. The method includes receiving a new video from one or more sensors and generating a new content graph (CG) based on the new video. The method also includes comparing the new CG with a plurality of prior CGs. The method further includes identifying a first portion of the new CG matching a portion of a first prior CG and a second portion of the new CG matching a portion of the second prior CG. The method further includes analyzing a first set of semantic annotations (SAs) associated with the portion of the first prior CG and a second set of SAs associated with the portion of the second prior CG. The method further includes generating a sequence of SAs for the new video based on the analysis of the first and the second set of SAs.

System and method for generating semantic annotations

The subject matter disclosed herein relates to X-ray imaging systems, and more specifically to digital X-ray imaging systems. In one embodiment, an imaging system includes an X-ray source configured to emit X-rays. The imaging system also includes an X-ray detector configured to detect the emitted X-rays and produce a corresponding electrical signal. The imaging system also includes a gantry configured to at least partially revolve the X-ray source and the X-ray detector about a primary rotational axis. The X-ray detector is coupled to the gantry so that a diagonal of the X-ray detector is oriented substantially perpendicular to the primary rotational axis.

System and method for orienting an X-ray detector

Exemplary embodiments include methods, systems, and devices for enabling users to provide quality scores for indicating the quality of image analysis methods performed on images of biological tissue. An exemplary user interface displays results of an image analysis method performed on an image of biological tissue in an overlaid manner on an image of biological tissue. The exemplary user interface enable a user to provide, directly on the user interface, one or more quality scores to indicate the user's assessment of the quality of the image analysis performed on the image. Exemplary embodiments store the quality scores provided by the user in association with the image analysis method and the image of biological tissue.

Systems and methods for performing quality review scoring of biomarkers and image analysis methods for biological tissue

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

Methods and systems for digitally enhancing an initial image of a material to which a plurality of stains were previously applied, that generally comprise: unmixing the image into a plurality of individual reconstructed images, each individual image corresponding to one of the stains; estimating a residual image corresponding to the difference between the original image and the reconstructed images; adjusting one or more components of the individual images; mixing the adjusted components using one or more estimated mixing coefficients; and adding the residual image to the mixed adjusted components to generate an enhanced image.

Methods and systems for digitally enhancing an image of a stained material

A method of segmenting a digital image of biological tissue includes accessing a ranking model calculated from training data representing shapes of conforming and non-conforming biological unit exemplars. The ranking model may include support vectors defining a hyperplane in a vector space. The method further includes accessing image data representing the digital image, identifying a first shape and a set of second constituent shapes in the digital image, wherein the first shape comprises a union of the set of second constituent shapes, determining a rank of a first data point in the image data corresponding to the first shape and a rank of a second data point in the image data corresponding to the set of second constituent shapes into the vector space, and segmenting the digital image using the first shape or the set of second constituent shapes based on which data point has a greater respective rank.

Biological unit identification based on supervised shape ranking

Systems and methods for segmenting images comprising cells, wherein the images comprise a plurality of pixels; one or more three dimensional (3D) clusters of cells are identified in the images; and the 3D clusters of cells are automatically segmented into individual cells using one or more models.

Methods and systems for automated segmentation of dense cell populations

Systems and methods for extracting information about an organism of interest, such as an atlas of the organism of interest; a storage device for at least temporarily storing an image of the organism of interest; and an operating device that automatically creates a map of the image of the organism of interest and automatically compares the map of the image to the atlas of the organism.

Systems and methods for automated extraction of high-content information from whole organisms

This invention, which provides a method for detecting a corruption in an image acquired from a biological sample, includes: providing at least one image of at least one cell; generating the image of the at least one cell over a period of time; determining if the at least one image of the at least one cell is corrupted; applying a wavelet transform, Fourier transform, or other frequency decomposing transform to the at least one image to decompose the at least one image into a plurality of sub-images, wherein the plurality of sub-images have a plurality of low frequency channels, a plurality of middle frequency channels and a plurality of high frequency channels; calculating a ratio based on an energy level of the plurality of low frequency channels and the plurality of middle frequency channels; and removing the at least one image of at least one cell if the at least one image is corrupted.

System and method for detecting and eliminating one or more defocused or low contrast-to-noise ratio images

The present invention is directed to method and system for image processing of test wells on a microplates wherein the microplates' test well wall boundaries are identified through the use of a candidate edge image wherein the candidate edge image represents locations of one or more segments of the wall boundaries.

Methods and systems for identifying well wall boundaries of microplates

A device and method for processing an image to create appearance and shape labeled images of a person or object captured within the image. The appearance and shape labeled images are unique properties of the person or object and can be used to re-identify the person or object in subsequent images. The appearance labeled image is an aggregate of pre-stored appearance labels that are assigned to image segments of the image based on calculated appearance attributes of each image segment. The shape labeled image is an aggregate of pre-stored shape labels that are assigned to image segments of the image based on calculated shape attributes of each image segment. An identifying descriptor of the person or object can be computed based on both the appearance labeled image and the shape labeled image. The descriptor can be compared with other descriptors of later captured images to re-identify a person or object.

Identifying descriptor for person and object in an image

Growing community of inventors

Ballston Lake, NY, United States of America

Jens Rittscher