General Electric Company

Ge Medical Systems Global Technology Company, LLC

Ge Precision Healthcare LLC

Molecular Devices Corporation

University of Washington

Ge Medical Systems Information Technologies Gmbh

Ge Medical Systems Global Technology, LLC

Ge Medical Systems

Ge Medical Systems Group, LLC

Gopal Biligeri Avinash

Saad Ahmed Sirohey

John Michael Sabol

Kadri Nizar Jabri

Min Zhang

Ravi Soni

Tao Tan

Jiang Hsieh

Renuka Uppaluri

Gireesha Chinthamani Rao

Cherik T Bulkes

Zhongmin Steve Lin

Prathyusha K Salla

Ananth P Mohan

Sandeep Dutta

Baojun Li

Matthew Joseph Walker

Lehel Ferenczi

John Skinner

Pál Tegzes

German Guillermo Vera Gonzalez

Patricia Le Nezet

David Thomas Gering

Tamanna Nanavaty Bembenek

Deann Marie Haas

Carson Hale Thomas

Zili Ma

Zita Herczeg

Rakesh Mullick

Laurent Launay

Renaud Capolunghi

Patrick Michael Virtue

Dibyajyoti Pati

Venkata Ratnam Saripalli

Jeffrey Wayne Eberhard

Jerome F Knoplioch

Krishna Seetharam Shriram

Vikram Reddy Melapudi

Katelyn Rose Nye

Stephen Wayne Metz

Zhye Yin

Xin Wang

Bruno De Man

Vianney Pierre Battle

Bipul Das

Rakesh Mohan Lal

Francois Serge Nicolas

Amber Elaine Rader

Bernhard Erich Hermann Claus

John Patrick Kaufhold

Matthew William Turek

Pal Tegzes

Rajeev Ramankutty Marar

Rowland F Saunders

Paul Edgar Licato

Vincent Jonas Bismuth

Steven Fors

Dejun Wang

Wen Jin

Tin-Su Pan

Soumya Ghose

Zoltan Kiss

Yaan Ge

Sohan Rashmi Ranjan

John David Hoford

Pinaki Ghosh

Utkarsh Agrawal

Alec Joseph Baenen

Thomas L Toth

James Zhengshe Liu

Xiaoye Wu

Kenneth Scott Kump

Christopher B Austin

Michael J Washburn

Peter Traneus Anderson

Sylvain Bernard

Sandeep Narendra Gupta

Jason Andrew Polzin

Joseph K Maier

Marc Thomas Edgar

Erik Normann Steen

Christopher David Unger

Robert Michael Vavrek

Bernice Hoppel

Shankara Bonthu Reddy

Scott Thomas Mansell

Abdalmajeid Musa Alyassin

Steven P Roehm

David Charles Mack

David Matthew Deaven

Musodiq O Bello

Mohamed Ali Hamadeh

Chuande Liu

Matthieu Denis Ferrant

Kelly Lynn Piacsek

Mukta Chandrashekhar Joshi

Steven J Huff

Jérôme Francois Knoplioch

Travis Frosch

Adam Budde

Systems/techniques that facilitate improved deep learning image processing are provided. In various embodiments, a system can access a medical image, wherein pixels or voxels of the medical image can be allocated among a plurality of regions. In various aspects, the system can generate, via execution of a deep learning neural network on the medical image, a set of region-wise parameter maps, wherein a region-wise parameter map can consist of one predicted parameter per region of the medical image. In various instances, the system can generate a transformed version of the medical image by feeding the set of region-wise parameter maps to an analytical transformation function. In various cases, the system can render the transformed version of the medical image on an electronic display. In various aspects, the plurality of regions can be irregular or tissue-based.

Deep learning image processing via region-wise parameter maps

Techniques are described for generating mono-modality training image data from multi-modality image data and using the mono-modality training image data to train and develop mono-modality image inferencing models. A method embodiment comprises generating, by a system comprising a processor, a synthetic 2D image from a 3D image of a first capture modality, wherein the synthetic 2D image corresponds to a 2D version of the 3D image in a second capture modality, and wherein the 3D image and the synthetic 2D image depict a same anatomical region of a same patient. The method further comprises transferring, by the system, ground truth data for the 3D image to the synthetic 2D image. In some embodiments, the method further comprises employing the synthetic 2D image to facilitate transfer of the ground truth data to a native 2D image captured of the same anatomical region of the same patient using the second capture modality.

Multimodality image processing techniques for training image data generation and usage thereof for developing mono-modality image inferencing models

Methods and systems are provided for inferring thickness and volume of one or more object classes of interest in two-dimensional (2D) medical images, using deep neural networks. In an exemplary embodiment, a thickness of an object class of interest may be inferred by acquiring a 2D medical image, extracting features from the 2D medical image, mapping the features to a segmentation mask for an object class of interest using a first convolutional neural network (CNN), mapping the features to a thickness mask for the object class of interest using a second CNN, wherein the thickness mask indicates a thickness of the object class of interest at each pixel of a plurality of pixels of the 2D medical image; and determining a volume of the object class of interest based on the thickness mask and the segmentation mask.

System and methods for inferring thickness of object classes of interest in two-dimensional medical images using deep neural networks

Various methods and systems are provided for denoising images. In one example, a method includes obtaining an input image and a noise map representing noise in the input image, generating, from the noise map and based on a calibration factor, a strength map, entering the input image and the strength map as input to a denoising model trained to output a denoised image based on the input image and the strength map, and displaying and/or saving the denoised image output by the denoising model.

Methods and systems for image denoising

An image processing system is provided. The image processing system includes a display, a processor, and a memory. The memory stores processor-executable code that when executed by the processor causes receiving images of a region of interest of a patient with an enteric tube or line disposed within the region of interest, detecting the medical tube or line within the image, generating a combined image by combining the received images, and superimposing graphical markers on the combined image that indicate placement or misplacement of the enteric tube or line, and displaying the combined image on a display. In further aspects, a classification of the enteric tube or line (e.g., correctly placed tube present, malpositioned tube present, and so forth) and a detected positional change in the placement of the enteric tube or line may be determined and communicated to one or more clinicians. Additionally, the outputs of the image processing system may also be provided to facilitate triage of patients, helping prioritize which tube placements require further attention and in what order.

System and method for visualizing placement of a medical tube or line

The invention relates to a method for a computer device, for detecting an optimal candidate compound based on a plurality of samples comprising a cell line and one or more biomarkers, and a plate map configuration, wherein the plate map configuration is providing locations of samples comprising cell lines exposed to one or more biomarkers and different concentrations of a candidate compound forming at least one concentration gradient, the candidate compound being comprised in a plurality of can-didate compounds, said method comprising generating () phenotypic profiles of each concentration gradient of each of the plurality of candidate compounds at a plurality of successive points in time to form a plurality of compound profiles, wherein generating phenotypic profiles comprises the steps obtaining () image data depicting each sample comprised in the concentration gradient, generating () a class-label and a class for each cell of the samples based on the image data, detecting () the optimal candidate compound by evaluating a comparison criterion on the plurality of compound profiles. Furthermore, the invention also relates to corre-sponding computer device, a computer program, and a computer program product.

Computer device for detecting an optimal candidate compound and methods thereof

A dual energy x-ray imaging system and method of operation includes an artificial intelligence-based motion correction system to minimize the effects of motion artifacts in images produced by the imaging system. The motion correction system is trained to apply simulated motion to various objects of interest within the LE and HE projections in the training dataset to improve registration of the LE and HE projections. The motion correction system is also trained to enhance the correction of small motion artifacts using noise attenuation and subtraction image-based edge detection on the training dataset images reduce noise from the LE projection, consequently improving small motion artifact correction. The motion correction system additionally employs separate motion corrections for soft and bone tissue in forming subtraction soft tissue and bone tissue images, and includes a motion alarm to indicate when motion between LE and HE projections requires a retake of the projections.

Artificial intelligence-based dual energy x-ray image motion correction training method and system

Techniques are described that facilitate dynamic multimodal segmentation selection and fusion in medical imaging. In one example embodiment, a computer processing system receives a segmentation dataset comprising a combination of different image segmentations of an anatomical object of interest respectively segmented via different segmentation models from different medical images captured of the (same) anatomical object, wherein the different medical images and the different image segmentations vary with respect to at least one of, capture modality, acquisition protocol, or acquisition parameters. The system employs a dynamic ranking protocol as opposed to a static ranking protocol to determine ranking scores for the different image segmentations that control relative contributions of the different image segmentations in association with combining the different image segmentations into a fused segmentation for the anatomical object. The system further combines the different image segmentations based on the ranking scores to generate the fused image segmentation.

Dynamic multimodal segmentation selection and fusion

An artificial intelligence (AI) X-ray image information detection and correction system is employed either as a component of the X-ray imaging system or separately from the X-ray imaging system to automatically scan post-exposure X-ray images to detect various types of information or characteristics of the X-ray image, including, but not limited to, anatomy, view, orientation and laterality of the X-ray image, along with an anatomical landmark segmentation. The information detected about the X-ray image can then be stored by the AI system in association with the X-ray image for use in various downstream X-ray system workflow automations and/or reviews of the X-ray image.

System and method for automatic detection and correction of anatomy, view, orientation and laterality in X-ray image for X-ray imaging system

An image processing system is provided. The image processing system includes a display, a processor, and a memory. The memory stores processor-executable code that when executed by the processor causes receiving an image of a region of interest of a patient with an enteric tube or line disposed within the region of interest, detecting the medical tube or line within the image, generating a combined image by superimposing graphical markers on the image that indicate placement or misplacement of the enteric tube or line, and displaying the combined image on a display. In further aspects, a classification of the enteric tube or line (e.g., correctly placed tube present, malpositioned tube present, and so forth) may be determined and communicated to one or more clinicians. Additionally, the outputs of the image processing system may also be provided to facilitate triage of patients, helping prioritize which tube placements require further attention and in what order.

Various methods and systems are provided for automatically registering and stitching images. In one example, a method includes entering a first image of a subject and a second image of the subject to a model trained to output a transformation matrix based on the first image and the second image, where the model is trained with a plurality of training data sets, each training data set including a pair of images, a mask indicating a region of interest (ROI), and associated ground truth, automatically stitching together the first image and the second image based on the transformation matrix to form a stitched image, and outputting the stitched image for display on a display device and/or storing the stitched image in memory.

Methods and systems for image registration

A tomosynthesis machine allows for faster image acquisition and improved signal-to-noise by acquiring a projection attenuation data and using machine learning to identify a subset of the projection attenuation data for the production of thinner slices and/or higher resolution slices using machine learning.

X-ray tomosynthesis system providing neural-net guided resolution enhancement and thinner slice generation

Systems/techniques that facilitate multi-layer image registration are provided. In various embodiments, a system can access a first image and a second image. In various aspects, the system can generate, via execution of a machine learning model on the first image and the second image, a plurality of registration fields and a plurality of weight matrices that respectively correspond to the plurality of registration fields. In various instances, the system can register the first image with the second image based on the plurality of registration fields and the plurality of weight matrices.

Multi-layer image registration

Techniques are described for optimizing deep learning model performance using image harmonization as a pre-processing step. According to an embodiment, a method comprises decomposing, by a system operatively coupled to a processor, an input image into sub-images. The method further comprises harmonizing the sub-images with corresponding reference sub-images of at least one reference image based on two or more different statistical values respectively calculated for the sub-images and the corresponding reference-sub images, resulting in transformation of the sub-images into modified sub-images images. In some implementations, the modified sub-images can be combined into a harmonized image having a more similar appearance to the at least one reference image relative to the input image. In other implementations, harmonized images and/or modified sub-images generated using these techniques can be used as ground-truth training samples for training one or more deep learning model to transform input images with appearance variations into harmonized images.

Image harmonization for deep learning model optimization

Methods and systems are provided for tuning a static model with multiple operating points to adjust model performance without retraining the model or triggering a new regulatory clearance. In one embodiment, a method comprises, responsive to a request to tune a model, obtaining a tuning dataset including a set of medical images, executing the model using the set of medical images as input to generate model tuning output, and determining, for each operating point of a set of operating points, a set of tuning metric values based on the tuning dataset and the model tuning output relative to each operating point. An operating point from the set of operating points may be selected based on each set of tuning metric values and, upon a request to analyze a subsequent medical image, a representation of a finding output from the static model executed at the selected operating point.

Methods and systems for tuning a static model

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New Berlin, WI, United States of America

Gopal Biligeri Avinash