General Electric Company

Lockheed Martin Corporation

Oliver Crispin Robotics Limited

Ge Precision Healthcare LLC

James Vradenburg Miller

Peter Henry Tu

Peter Michael Edic

Ricardo Scott Avila

Walter Vincent Dixon, Iii

Thomas Baby Sebastian

Paulo Ricardo Dos Santos Mendonca

Matthew William Turek

Bruno Kristiaan Bernard De Man

Jed Douglas Pack

Robert F Senzig

Ravindra Mohan Manjeshwar

Walter V Dixon

Andrew Crispin Graham

Xiaoye Wu

Adrian F Warner

Glen William Brooksby

Daniel Richard Schneidewend

David Allen Langan

Robert August Kaucic

Hao Lai

Joseph Leagrand Mundy

Vamshi Krishna Reddy Kommareddy

Srikanth Suryanarayanan

Paulo Ricardo Mendonca

Sathish Ramani

Xiaodong Tao

Donald Wagner Hamilton

Maxime Cazalas

Wesley David Turner

Yen-Liang Lin

Mohammed Yousefhussien

Xia Li

Biswajit Medhi

Hans-Peter Stoll

Jiang Hsieh

Samit Kumar Basu

Charles Burton Theurer

Bernhard Erich Hermann Claus

Darin Robert Okerlund

William E Lorensen

Floribertus P M Heukensfeldt Jansen

Alexander Ganin

Joseph Manak

Arpit Jain

Xin Wang

Lin Fu

Rahul Bhotika

Deborah Joy Walter

Boris Yamrom

Prem Venugopal

Dirk Ryan Padfield

Bruno Kristiaan Bernard DeMan

Abdalmajeid Musa Alyassin

Maria Iatrou

William Macomber Leue

Colin Craig McCulloch

Yogisha Mallya

Floribertus Philippus Martinus Heukensfeldt Jansen

Mukta Chandrashekhar Joshi

Floribertus Heukensfeldt Jansen

Dinko Eduardo Gonzalez Trotter

Victor Nzomigni

Christopher Richard Volpe

Arunabha Shasanka Roy

Russell Scott Blue

Eri Haneda

Ajay Gopinath

James Chapman Ross

Hui He

Prasad Thapa

Bernhard Claus

Tony Chishao Pan

Achalesh Kumar

Steven Eric Lithicum

Abdalmajeid M Alyassin

Nihaar Shah

Dinko Gonzalez Trotter

Paulo Ricardo (dos Santos) Mendonca

Tony Chi-shao Pan

Peter Michael Edie

A pulse illumination imaging system is provided. The system includes an image sensor, a light source, and a controller. The image sensor includes a plurality of light sensitive pixel elements that are activatable for a designated exposure time to capture one or more images. The controller is configured to determine an activation time to activate the image sensor based on motion of a target element relative to the image sensor; activate the image sensor at the activation time; and activate the light source during the exposure time of the image sensor to produce a pulse having a preconfigured time duration that is less than the exposure time of the image sensor.

Pulse illumination imaging of a target element

Systems and methods for engine imaging are provided. An imaging system includes an optical sensor coupled to an insertion tool and a light source configured to output alight projection onto a component of the engine assembly. The system includes a processor configured to: receive data comprising a plurality of frames from the optical sensor captured while the component of the engine assembly is rotating, determine an angular displacement of the component between frames, and form a 3D point cloud of the component based on combining the data in the frames based on the angular displacement of the component between the frames.

3D imaging for engine assembly inspection

A control circuit accesses three-dimensional image information for a given three-dimensional object. The control circuit accesses a selection corresponding to a feature of the three-dimensional object, and then automatically generates a plurality of synthetic images of the three-dimensional object as a function of the three-dimensional and the selection of the aforementioned feature. By one approach, these synthetic images include supplemental visual emphasis corresponding to the aforementioned feature. The generated plurality of synthetic images can then be used as a training corpus when training a machine learning model.

Machine learning model training corpus apparatus and method

According to some embodiments, a system and method are provided comprising a vegetation module to receive image data from an image source; a memory for storing program instructions; a vegetation processor, coupled to the memory, and in communication with the vegetation module, and operative to execute program instructions to: receive image data; estimate a vegetation segmentation mask; generate at least one of a 3D point cloud and a 2.5D Digital Surface Model based on the received image data; estimate a relief surface using a digital terrain model; generate a vegetation masked digital surface model based on the digital terrain model, the vegetation segmentation mask and at least one of the 3D point cloud and the 2.5D DSM; generate a canopy height model based on the generated vegetation masked digital surface model; and generate at least one analysis with an analysis module, wherein the analysis module receives the generated canopy height model prior to execution of the analysis module, and wherein the analysis module uses the generated canopy height model in the generation of the at least one analysis. Numerous other aspects are provided.

System and method for vegetation modeling using satellite imagery and/or aerial imagery

According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising an imagery data source storing image data from a plurality of images; a ground point module; a memory storing program instructions; and a ground point processor, coupled to the memory, and in communication with the ground point module and operative to execute the program instructions to: receive image data for an area of interest (AOI); generate a digital surface map from the received image data, wherein the digital surface map includes an elevation value for each of a plurality of points on the digital surface map; generate a ground point sampling based on the elevation values for the plurality of points on the digital surface map; generate an image boundary sampling based on elevation values for the plurality of points along a plurality of edges of the area of interest; and interpolate the generated ground point sampling and the image boundary sampling to generate a digital terrain map. Numerous other aspects are provided.

Remote sensing method to model terrain shape by detecting reliable ground points

A computer-implemented method for image processing is provided. The method includes obtaining data acquired by a medical imaging system. The method also includes normalizing the data. The method further includes de-noising the normalized data utilizing a deep learning-based denoising network. The method even further includes de-normalizing the de-noised data. The method yet further includes generating blended data based on both the data and the de-normalized de-noised data.

System and method for normalizing dynamic range of data acquired utilizing medical imaging

In the present invention, a system and method for selection of an optimal catheter for use in a medical procedure relative to the anatomy of a patient includes the steps of providing a system including a scanning device capable of obtaining image data on a ROI within the anatomy of a patient and reconstructing a 3D image of the ROI from the image data, a display capable of illustrating the 3D image and a 3D catheter model, and a CPU operably connected to the scanning device and the display and operable to analyze the 3D image in comparison with the 3D catheter model, obtaining image data of the ROI of the patient, reconstructing a 3D image of the ROI from the image data and comparing the 3D catheter model with the 3D image of the ROI to determine the catheter with the optimal configuration for use in the procedure.

System and method for optimal catheter selection for individual patient anatomy

According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising a 3D building modeling module; a memory for storing program instructions; a 3D building modeling processor, coupled to the memory, and in communication with the 3D building modeling module and operative to execute program instructions to: receive a region of interest; receive an image of the region of image from a data source; generate a surface model based on the received image including one or more buildings; generate a digital height model; decompose each building into a set of shapes; apply a correction process to the set of shapes; execute a primitive classification process to each shape; execute a fitting process to each classified shape; select a best fitting model; and generate a 3D model of each building. Numerous other aspects are provided.

Primitive-based 3D building modeling, sensor simulation, and estimation

According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising an image data source storing image data from a plurality of images; a height map source storing height maps for an area of interest (AOI); a building footprint module; a memory; and a building footprint processor, operative to execute the program instructions to: receive image data for an AOI; receive a height map for the AOI; execute a building segmentation module to generate a building mask that indicates a presence of one or more buildings in the AOI; apply at least one clean mask process to the generated building mask to generate a clean mask; receive the clean mask at an instance building segmentation module; and execute the instance building segmentation module to generate at least one building footprint based on the clean mask and the received image data. Numerous other aspects are provided.

Building footprint generation by using clean mask generation and received image data

The present disclosure relates to training one or more neural networks for vascular vessel assessment using synthetic image data for which ground-truth data is known. In certain implementations, the synthetic image data may be based in part, or derived from, clinical image data for which ground-truth data is not known or available. Neural networks trained in this manner may be used to perform one or more of vessel segmentation, decalcification, Hounsfield unit scoring, and/or estimation of a hemodynamic parameter.

Deep learning for arterial analysis and assessment

A method for characterizing anatomical features includes receiving scanned data and image data corresponding to a subject. The scanned data comprises sinogram data. The method further includes identifying a first region in an image of the image data corresponding to a region of interest. The method also includes determining a second region in the scanned data. The second region corresponds to the first region. The method further includes identifying a sinogram trace corresponding to the region of interest. The sinogram trace comprises sinogram data present within the second region. The method includes determining a data feature of the subject based on the sinogram trace and a deep learning network. The method also includes determining a diagnostic condition corresponding to a medical condition of the subject based on the data feature.

System and method for characterizing anatomical features

Embodiments of systems, methods and non-transitory computer readable media for imaging are presented. Preliminary image data corresponding to a first FOV of a subject at a first resolution is acquired using an imaging system including one or more radiation sources and at least one hybrid detector, specifically using at least one section of the hybrid detector having the first resolution. The target ROI is identified using the preliminary image data. Further, the subject is positioned to align the target ROI along a designated axis. Additionally, parameters associated with the sources, the hybrid detector and/or an imaging system gantry are configured for acquiring target image data at a second resolution greater than the first resolution using at least one section of the hybrid detector having the second resolution. Further, one or more images corresponding to at least the target ROI are reconstructed using the target and/or the preliminary image data.

Method and system for non-invasive imaging of a target region

A technique is provided for extracting one or more features of interest from one or more projection images. The technique includes accessing projection images comprising at least one feature of interest enhanced by a contrast agent, generating a contrast agent null image based on the projection images, generating a bone mask based on the contrast agent null image, and generating a bone extracted image based on the bone mask.

System and method for extracting features of interest from an image

A method and system for registering two images is described. The method comprises synthesizing projections from two volumetric images to be registered, estimating a plurality of two dimensional (2D) deformable fields from the projections and generating a three dimensional (3D) deformable fields using a plurality of backprojections of the 2D deformable fields.

Image registration system and method

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Clifton Park, NY, United States of America

James Vradenburg Miller