Siemens Healthcare Gmbh

Siemens Aktiengesellschaft

Siemens Medical Solutions Usa, Inc.

Siemens Corporation

Siemens Corporate Research, Inc.

Other

Siemens Healthineers Ag

Rutgers, the State University of New Jersey

Siemens Energy, Inc.

Yale University

Siemens Canada Limited

Stevens Institute of Technology

Technische Universität München

Siemens Vdo Automotive Ag

National Institutes of Health

Dorin Comaniciu

Bogdan Georgescu

Yefeng Zheng

Ali Kamen

Tommaso Mansi

Shaohua Kevin Zhou

Puneet Sharma

Sasa Grbic

Terrence Chen

Xiang Sean Zhou

David D Liu

Saikiran Rapaka

Razvan Ioan Ionasec

Jan Boese

Adrian Barbu

Tiziano Passerini

Lucian Mihai Itu

Visvanathan Ramesh

Matthias John

Viorel Mihalef

Ingmar Voigt

Vivek Kumar Singh

Ying Zhu

Razvan Ionasec

Fernando Vega-Higuera

Gareth Funka-Lea

Peter Durlak

Michael Scheuering

Daguang Xu

Martin Ostermeier

Helene C Houle

Wei Zhang

Luca Bogoni

Dominik Neumann

Thomas Redel

Zhoubing Xu

Dominik Bernhardt

R Bharat Rao

Xiaoguang Lu

Norbert Strobel

Wen Wu

Jingdan Zhang

Bin Lou

Frank Sauer

Sriram Krishnan

Ankur Kapoor

Michael Suehling

Arun Krishnan

Michael Wels

Mariappan S Nadar

Atilla Peter Kiraly

Amit Chakraborty

Shun P Miao

Edgar Mueller

Stefan Kluckner

Michael Kelm

Thomas Flohr

Shanhui Sun

Boris Mailhe

Benjamin L Odry

Berthold Kiefer

Rui Liao

Marie-Pierre Jolly

Grzegorz Soza

John I Jackson

Marcos Salganicoff

Maneesh Kumar Singh

Jing Xiao

Michal Sofka

Justinian Rosca

Li Zhang

Daphne Yu

Klaus Engel

Günter Lauritsch

Estelle Camus

Gianluca Paladini

Mehmet Akif Gulsun

Kaloian Petkov

Christopher Rohkohl

Stefan Böhm

Noha Youssry El-Zehiry

Leo John Grady

Peter Speier

Marcus Pfister

Andreas Greiser

Thomas Mertelmeier

Carol L Novak

Bernhard Geiger

Maximilian Fleischer

Steffen Kappler

Liexiang Fan

Gabriel Haras

Anna Jerebko

Michael J Metala

Kai Ma

Simon Arberet

Radu Victor Balan

Michael G Cannon

Martin Harder

Andreas Keller

Similar pre-stored medical datasets are identified by comparison with a current case dataset. A current case dataset is provided and includes radiological data of a patient. A number of pre-stored medical datasets each including radiological data of other patients are provided. Each case dataset is evaluated according to a predefined AI-based method to obtain a number of definitive features for that case dataset. The definitive features of the current case dataset are compared with the definitive features of each pre-stored medical dataset to identify a number of pre-stored medical datasets most similar to the current case dataset. The identified number of most similar pre-stored medical datasets are output.

Method and arrangement for identifying similar pre-stored medical datasets

A method is for generating modified medical images. An embodiment of the method includes receiving a first medical image displaying an abnormal structure within a patient, and applying a trained inpainting function to the first medical image to generate a modified first medical image, the trained inpainting function being trained to inpaint abnormal structures within a medical image. The method includes determining an abnormality patch based on the first medical image and the modified first medical image; receiving a second medical image of the same type as the first medical image; and including the abnormality patch into the second medical image to generate a modified second medical image. A method is for detecting abnormal structures using a trained detection function trained based on modified second medical images. Systems, computer programs and computer-readable media related to those methods are also disclosed.

Generating modified medical images and detecting abnormal structures

Systems and methods for generating a synthetic image are provided. An input medical image in a first modality is received. A synthetic image in a second modality is generated from the input medical image. The synthetic image is upsampled to increase a resolution of the synthetic image. An output image is generated to simulate image processing of the upsampled synthetic image. The output image is output.

Generating synthetic x-ray images and object annotations from CT scans for augmenting x-ray abnormality assessment systems

For reconstruction of an image in MRI, unsupervised training (i.e., data-driven) based on a scan of a given patient is used to reconstruct model parameters, such as estimating values of a contrast model and a motion model based on fit of images generated by the models for different readouts and times. The models and the estimated values from the scan-specific unsupervised training are then used to generate the patient image for that scan. This may avoid artifacts from binning different readouts together while allowing for scan sequences using multiple readouts.

Reconstruction in magnetic resonance imaging with image representations as implicit functions in time

Systems and methods for predicting risk for a medical event associated with evaluating or treating a patient for a disease are provided. Input medical imaging data and patient data of a patient are received. The input medical imaging data includes abnormality patterns associated with a disease. Imaging features are extracted from the input medical imaging data using a trained machine learning based feature extraction network. One or more of the extracted imaging features are normalized. The one or more normalized extracted imaging features and the patient data are encoded into features using a trained machine learning based encoder network. Risk for a medical event associated with evaluating or treating the patient for the disease is predicted based on the encoded features.

Risk prediction for COVID-19 patient management

Systems and methods for generating a synthesized medical image are provided. An input medical image is received. A synthesized segmentation mask is generated. The input medical image is masked based on the synthesized segmentation mask. The masked input medical image has an unmasked portion and a masked portion. An initial synthesized medical image is generated using a trained machine learning based generator network. The initial synthesized medical image includes a synthesized version of the unmasked portion of the masked input medical image and synthesized patterns in the masked portion of the masked input medical image. The synthesized patterns is fused with the input medical image to generate a final synthesized medical image.

Medical image synthesis of abnormality patterns associated with COVID-19

For machine learning for abnormality assessment in medical imaging and application of a machine-learned model, the machine learning uses regularization of the loss, such as regularization being used for training for abnormality classification in chest radiographs. The regularization may be a noise and/or correlation regularization directed to the noisy ground truth labels of the training data. The resulting machine-learned model may better classify abnormalities in medical images due to the use of the noise and/or correlation regularization in the training.

Machine learning from noisy labels for abnormality assessment in medical imaging

Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Method and system for image registration using an intelligent artificial agent

Systems and methods for assessing a disease are provided. An input medical image in a first modality is received. Lungs are segmented from the input medical image using a trained lung segmentation network and abnormality patterns associated with the disease are segmented from the input medical image using a trained abnormality pattern segmentation network. The trained lung segmentation network and the trained abnormality pattern segmentation network are trained based on 1) synthesized images in the first modality generated from training images in a second modality and 2) target segmentation masks for the synthesized images generated from training segmentation masks for the training images. An assessment of the disease is determined based on the segmented lungs and the segmented abnormality patterns.

Assessment of abnormality patterns associated with COVID-19 from x-ray images

Patient, user, and/or AI information are used in a multi-objective optimization to select one of a plurality of available AIs for a task. On a patient or user-specific basis, an optimal AI is selected and applied for medical imaging or other healthcare actions. The selection may be before application, avoiding costs of applying multiple AIs to obtain the best results. The optimization may be based on statistical feedback from the user for various of the available AIs, providing information not otherwise available. The optimization may be based on AI performance, AI inclusion and/or exclusion criteria, and/or pricing information. By using optimization based on various information related to the patient, user, and/or available AI, the application of AI for a given user and/or patient by the computer may be improved. The computer operates better to provide more focused information through AI application.

Artificial intelligence dispatch in healthcare

Systems and methods are provided for classifying an abnormality in a medical image. An input medical image depicting a lesion is received. The lesion is localized in the input medical image using a trained localization network to generate a localization map. The lesion is classified based on the input medical image and the localization map using a trained classification network. The classification of the lesion is output. The trained localization network and the trained classification network are jointly trained.

Localization and classification of abnormalities in medical images

A computer-implemented method for executing patient management workflows includes acquiring a pre-test dataset of clinically relevant information related to a patient and using a first intelligent agent to identify a diagnostic test for the patient based on the pre-test dataset. Following performance of the diagnostic test, a second intelligent agent is used to select a processing technique to be applied to data collected from the diagnostic test to obtain a diagnostic marker. Following application of the processing technique to the data collected from the diagnostic test, a third intelligent agent is used to generate an optimal patient management plan based on the pre-test dataset, the data collected from the diagnostic test, and the diagnostic marker.

Intelligent agents for patient management

Rather than rely on variation from physician to physician and limited imaging information for assessing plaque vulnerability of a patient, medical imaging and other information are used by a machine-implemented classifier to predict plaque rupture. Anatomical, morphological, hemodynamic, and biochemical features are used in combination to classify plaque.

Plaque vulnerability assessment in medical imaging

An artificial agent based cognitive operating room system and a method thereof providing automated assistance for a surgical procedure are disclosed. Data related to the surgical procedure from multiple data sources is fused based on a current context. The data includes medical images of a patient acquired using one or more medical imaging modalities. Real-time quantification of patient measurements based on the data from the multiple data sources is performed based on the current context. Short-term predictions in the surgical procedure are forecasted based on the current context, the fused data, and the real-time quantification of the patient measurements. Suggestions for next steps in the surgical procedure and relevant information in the fused data are determined based on the current context and the short-term predictions. The suggestions for the next steps and the relevant information in the fused data are presented to an operator.

System and method for artificial agent based cognitive operating rooms

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Princeton Junction, NJ, United States of America

Dorin Comaniciu