Shanghai United Imaging Intelligence Co., Ltd.

Siemens Healthcare Gmbh

Siemens Aktiengesellschaft

Siemens Healthcare Diagnostics Gmbh

Siemens Corporation

Other

Siemens Medical Solutions Usa, Inc.

Siemens Energy, Inc.

Siemens Mobility Gmbh

Friedrich-alexander-universitat Erlangen-nürnberg

United Imaging Intelligence (beijing) Co., Ltd.

Siemens Healthineers Ag

Terrence Chen

Shanhui Sun

Zhang Chen

Yao-Jen Chang

Dorin Comaniciu

Vivek Kumar Singh

Stefan Kluckner

Kai Ma

Benjamin S Pollack

Ziyan Wu

Yikang Liu

Wen Wu

Arun Innanje

Birgi Tamersoy

Norbert Strobel

Andreas Wimmer

Srikrishna Karanam

Michael Wels

Bogdan Georgescu

Puneet Sharma

Shaohua Kevin Zhou

David Y Liu

Xiao Chen

Ralf Nanke

Ali Kamen

Tommaso Mansi

Ying Zhu

Rui Liao

Shun P Miao

Grzegorz Soza

Gareth Funka-Lea

Viorel Mihalef

Xiaoguang Lu

Marcus Pfister

Atilla Peter Kiraly

Sasa Grbic

Tiziano Passerini

David D Liu

Maximilian Fleischer

Ankur Kapoor

Wei Zhang

Matthias John

Daphne Yu

Chao Yuan

Claus Neubauer

Günter Lauritsch

Joachim Hornegger

Yunqiang Chen

Jar-How Lee

Peng Wang

Abhishek Sharma

Martin Ostermeier

Jan Ernst

Peter Durlak

Meng Zheng

Andreas Krauss

Alexander Brost

Simone Prummer

Susanne Oepping

Patrick Wissmann

Klaus J Kirchberg

Yimo Guo

Ahmet Tuysuzoglu

Jiangping Wang

Guillaume Dumont

Oliver Lehmann

Alexander Michael Gigler

Brian Teixeira

Ruhan Sa

Susanne Dornberger

Kevin P Bailey

Benjamin Planche

Qiaoying Huang

Thomas Pohl

Eberhard Ritzhaupt-Kleissl

Thomas J O'Donnell

Andreas Meyer

Mingqing Chen

Anton Nekovar

Charles Henri Florin

Michael Fuhrmann

Jiang Tian

Ralph Grothmann

Olivier Ecabert

Markus Rossmeier

Martin Bischoff

Oliver Hennig

John Paulus, Jr

Guenter Lauritsch

Sara Good

Ulrich Bill

Oliver Baruth

Jeremy Ralph Wiles

Thoa Nong

Puyang Wang

Sarfaraz Hussein

Jeffrey Stoll

Johann Kim

A power management apparatus for a workflow to enable low power MR patient positioning on edge devices is disclosed. The power management apparatus changes an operational mode of an edge device from a first power mode to a second power mode after a defined time-interval. The power management apparatus further controls the edge device to capture a first image of a first scene. The power management apparatus further determines a trigger point based on a detection of a plurality of objects in the captured first image. The power management apparatus further changes the operational mode of the edge device from the second power mode to a third power mode to control a consumption of electric power while a set of operations is executed at the edge device. The operational mode of the edge device may be changed at the determined trigger point.

Apparatus and method to enable low power magnetic resonance patient positioning on edge devices

A person's privacy is protected by the law in many settings and disclosed herein are systems, methods, and instrumentalities associated with anonymizing an image of a person while still preserving the visual saliency and/or utility of the image for one or more downstream tasks. These objectives may be accomplished using various machine-learning (ML) techniques such as ML models trained for extracting identifying and residual features from the input image as well as ML models trained for transforming the identifying features into identity-concealing features and for preserving the utility features of the image. An output image may be generated based on the various ML models, wherein the identity of the person may be substantially disguised in the output image while the background and utility attributes of the original image may be substantially maintained in the output image.

Systems and methods for anonymizing images

An apparatus may obtain a sequence of medical images of a target structure and determine, using a first ANN, a first segmentation and a second segmentation of the target structure based on a first medical image and a second medical image, respectively. The first segmentation may indicate a first plurality of pixels that may belong to the target structure. The second segmentation may indicate a second plurality of pixels that may belong to the target structure. The apparatus may identify, using a second ANN, a first subset of true positive pixels among the first plurality of pixels that may belong to the target structure, and a second subset of true positive pixels among the second plurality of pixels that may belong to the target structure. The apparatus may determine a first refined segmentation and a second refined segmentation of the target structure based on the true positive pixels.

Systems and methods for segmenting a medical image sequence

Disclosed herein are systems, methods, and instrumentalities associated with magnetic resonance (MR) image reconstruction. An under-sampled MR image may be reconstructed through an iterative process (e.g., over multiple iterations) based on a machine-learning (ML) model. The ML model may be obtained through a reinforcement learning process during which the ML model may be used to predict a correction to an input MR image of at least one of the multiple iterations, apply the correction to the input MR image to obtain a reconstructed MR image, determine a reward for the ML model based on the reconstructed MR image, and adjust the parameters of the ML model based on the reward. The reward may be determined using a pre-trained reward neural network and the ML model may also be pre-trained in a supervised manner before being refined through the reinforcement learning process.

Under-sampled magnetic resonance image reconstruction of an anatomical structure based on a machine-learned image reconstruction model

Digital breast tomosynthesis (DBT) may provide richer information than full-field digital mammography (FFDM). DBT data such as DBT slices may be processed based on deep learning techniques such as using a neural network, and the DBT slices may be divided into groups and a pre-determined number of representative images may be derived based on the grouping. The neural network may be configured to process the representative images to predict the presence or non-presence of a breast disease such as breast cancer.

Systems and methods for processing breast slice images through an artificial neural network to predict abnormalities in breasts

Described herein are systems, methods, and instrumentalities associated with automatically annotating a 3D image dataset. The 3D automatic annotation may be accomplished based on a 2D annotation provided by an annotator and by propagating the 2D annotation through multiple images of a sequence of 2D images associated with the 3D image dataset. The automatically annotated 3D image dataset may then be used to annotate other 3D image datasets based on similarities between the first 3D image dataset and the other 3D image datasets. The automatic annotation of the first 3D image dataset and/or the other 3D image datasets may be conducted based on one or more machine-learning models trained for performing those tasks.

Systems and methods for annotating 3D data

Deep learning-based systems, methods, and instrumentalities are described herein for MRI reconstruction and/or refinement. An MRI image may be reconstructed based on under-sampled MRI information and a generative model may be trained to refine the reconstructed image, for example, by increasing the sharpness of the MRI image without introducing artifacts into the image. The generative model may be implemented using various types of artificial neural networks including a generative adversarial network. The model may be trained based on an adversarial loss and a pixel-wise image loss, and once trained, the model may be used to improve the quality of a wide range of 2D or 3D MRI images including those of a knee, brain, or heart.

MRI reconstruction based on generative models

Described herein are systems, methods, and instrumentalities associated with using a multi-layer perceptron (MLP) neural network to process medical images of an anatomical structure. The processing may include padding an input image in accordance with the training of the MLP neural network, splitting the input image (e.g., the padded input image) into patches of a same size, and processing the patches through the MLP neural network over one or more iterations. During an iteration of the processing, the patches may be processed separately and re-combined into an intermediate image before the intermediate image is shifted to concatenate portions of the image that are derived from different patches. This way, global features of the anatomical structure may be learned and used to improve the quality of the image generated by the MLP neural network, without incurring significant computation or memory costs.

Systems and methods for processing medical images with multi-layer perceptron neural networks

A method and a system for managing healthcare records of a user are provided. The method includes storing an electronic medical record related to the user in form of a non-fungible token (NFT) written to a blockchain, associating a smart contract to the NFT in the blockchain, authorizing a request to access the electronic medical record related to the user based on the defined ownership of the electronic medical record stored in the blockchain, identifying one or more NFTs from the blockchain comprising one or more electronic medical records related to the user based on processing of the identifier information in associated one or more smart contracts therewith, in response to the request, and sending the one or more electronic medical records corresponding to the identified one or more NFTs to a requestor associated with the request.

Patient care record management system

Deblurring and denoising a medical image such as X-ray fluoroscopy images may be challenging, and deep-learning based techniques may be employed to meet the challenge. An artificial neural network (ANN) may be trained using training images with synthetic noise and as well as training images with real noise. The parameters of the ANN may be adjusted during the training based on at least a first loss designed to maintain continuity between consecutive medical images generated by the ANN and a second loss designed to maintain similarity of patches inside a medical image generated by the ANN. The parameters of the ANN may be further adjusted based on a third loss that may be calculated from ground truth associated with the synthetic training images. Transfer learning between the synthetic images and the real images may be accomplished using these techniques.

Systems and methods for deblurring and denoising medical images

Described herein are systems, methods, and instrumentalities associated with segmenting and/or determining the shape of an anatomical structure. An artificial neural network (ANN) is used to perform these tasks based on a statistical shape model of the anatomical structure. The ANN is trained by evaluating and backpropagating multiple losses associated with shape estimation and segmentation mask generation. The model obtained using these techniques may be used for different clinical purposes including, for example, motion estimation and motion tracking.

Image segmentation and tracking based on statistical shape model

Described herein are systems, methods, and instrumentalities associated with using an invertible neural network to complete various medical imaging tasks. Unlike traditional neural networks that may learn to map input data (e.g., a blurry reconstructed MRI image) to ground truth (e.g., a fully-sampled MRI image), the invertible neural network may be trained to learn a mapping from the ground truth to the input data, and may subsequently apply an inverse of the mapping (e.g., at an inference time) to complete a medical imaging task. The medical imaging task may include, for example, MRI image reconstruction (e.g., to increase the sharpness of a reconstructed MRI image), image denoising, image super-resolution, and/or the like.

Systems and methods for processing medical images with invertible neural networks

A 3D anatomical model of one or more blood vessels of a patient may be obtained using CT angiography, while a 2D image of the blood vessels may be obtained based on fluoroscopy. The 3D model may be registered with the 2D image based on a contrast injection site identified on the 3D model and/or in the 2D image. A fused image may then be created to depict the overlaid 3D model and 2D image, for example, on a monitor or through a virtual reality headset. The injection site may be determined automatically or based on a user input that may include a bounding box drawn around the injection site on the 3D model, a selection of an automatically segmented area in the 3D model, etc.

Systems and methods for medical image fusion

Embodiments provide a method of using image-based tube top circle detection based on multiple candidate selection to localize the tube top circle region in input images. According to embodiments provided herein, the multi-candidate selection enhances the robustness of tube circle detection by making use of multiple views of the same tube to improve the robustness of tube top circle detection. With multiple candidates extracted from images under different viewpoints of the same tube, the multi-candidate selection algorithm selects an optimal combination among the candidates and provides more precise measurement of tube characteristics. This information is invaluable in an IVD environment in which a sample handler is processing the tubes and moving the tubes to analyzers for testing and analysis.

Image-based tube top circle detection with multiple candidates

An automated process for data annotation of medical images includes obtaining image data from an imaging sensor, partitioning the image data, identifying an object of interest in the partitioned image data, generating an initial contour with one or more control points with respect to the object of interest, identifying a manual adjustment of one of the control points, automatically adjust a position of at least one other control point within a predetermined range of the manually adjusted control point to a new position, the new position of the at least one other control point and manually adjusted control point defining a new contour, and generating an updated image with the new contour and corresponding control points.

Interactive contour refinements for data annotation

Growing community of inventors

Princeton, NJ, United States of America

Terrence Chen