Siemens Healthcare Gmbh

Siemens Aktiengesellschaft

Siemens Medical Solutions Usa, Inc.

Siemens Corporation

Siemens Corporate Research, Inc.

Siemens Healthineers Ag

Other

Rutgers, the State University of New Jersey

Siemens Energy, Inc.

Yale University

Siemens Canada Limited

Stevens Institute of Technology

Technische Universität München

Continental Automotive Gmbh

Siemens Healthcare Diagnostics Gmbh

Dorin Comaniciu

Bogdan Georgescu

Ali Kamen

Yefeng Zheng

Tommaso Mansi

Puneet Sharma

Shaohua Kevin Zhou

Sasa Grbic

Terrence Chen

Xiang Sean Zhou

David D Liu

Saikiran Rapaka

Razvan Ioan Ionasec

Jan Boese

Adrian Barbu

Tiziano Passerini

Lucian Mihai Itu

Vivek Kumar Singh

Visvanathan Ramesh

Ingmar Voigt

Matthias John

Viorel Mihalef

Ying Zhu

Razvan Ionasec

Fernando Vega-Higuera

Gareth Funka-Lea

Daguang Xu

Peter Durlak

Helene C Houle

Michael Scheuering

Bin Lou

Martin Ostermeier

Thomas Redel

Wei Zhang

Luca Bogoni

Zhoubing Xu

Dominik Neumann

Mariappan S Nadar

Dominik Bernhardt

Frank Sauer

R Bharat Rao

Ankur Kapoor

Xiaoguang Lu

Norbert Strobel

Wen Wu

Jingdan Zhang

Boris Mailhe

Sriram Krishnan

Michael Suehling

Arun Krishnan

Shun P Miao

Michael Wels

Thomas Flohr

Atilla Peter Kiraly

Amit Chakraborty

Rui Liao

Berthold Kiefer

Edgar Mueller

Stefan Kluckner

Michael Kelm

Shanhui Sun

Benjamin L Odry

Li Zhang

Marie-Pierre Jolly

John I Jackson

Grzegorz Soza

Marcos Salganicoff

Simon Arberet

Maneesh Kumar Singh

Jing Xiao

Andreas Greiser

Michal Sofka

Justinian Rosca

Daphne Yu

Mehmet Akif Gulsun

Klaus Engel

Günter Lauritsch

Estelle Camus

Gianluca Paladini

Kaloian Petkov

Christopher Rohkohl

Stefan Böhm

Noha Youssry El-Zehiry

Leo John Grady

Peter Speier

Marcus Pfister

Thomas Mertelmeier

Carol L Novak

Bernhard Geiger

Maximilian Fleischer

Steffen Kappler

Liexiang Fan

Gabriel Haras

Anna Jerebko

Michael J Metala

Kai Ma

Radu Victor Balan

Michael G Cannon

Martin Harder

Andreas Keller

For prediction of response of radiation therapy, radiomics are used for unsupervised machine training of an encoder-decoder network to predict based on input of image data, such as computed tomography image data and from segmentation. The trained encoder is then used to generate latent representations to be used as input to different classifiers or regressors for prediction of therapy responses, such as one classifier to predict response for an organ at risk and another classifier to predict another type of response for the organ at risk or to predict a response for the tumor.

Representation learning for organs at risk and gross tumor volumes for treatment response prediction

Systems and methods are provided for optimizing a deep learning model. A multi-site dataset associated with different clinical sites and a deployment dataset associated with a deployment clinical site are received. A deep learning model is trained based on the multi-site dataset. The trained deep learning model is optimized based on the deployment dataset. The optimized trained deep learning model is output.

Localization and classification of abnormalities in medical images

Systems and methods for generating synthesized medical images of a tumor are provided. A 3D mask of an anatomical structure generated from a 3D medical image and a 3D image of a plurality of concentric spheres are received. A 3D mask of a tumor is generated based on the 3D mask of the anatomical structure and the 3D image of the plurality of concentric spheres using a first 3D generator network. A 3D intensity map of the tumor is generated based on the 3D mask of the tumor and the 3D image of the plurality of concentric spheres using a second 3D generator network. A 3D synthesized medical image of the tumor is generated based on one or more 2D slices of the 3D intensity map of the tumor and one or more 2D slices of the 3D medical image using a 2D generator network. The 3D synthesized medical image of the tumor is output.

Synthesis of medical images of brain tumors using 3D-2D GANs

A scheduling system includes: a plurality of input devices configured to output medical data, a workforce storage, configured to store working characteristics of a plurality of doctors, and a scheduler configured to receive as input data related to the medical data and the working characteristics, and configured to provide as output a plurality of schedules for the plurality of doctors for analysing the medical data.

Medical data management system, method and computer program

For cardiac flow detection in echocardiography, by detecting one or more valves, sampling planes or flow regions spaced from the valve and/or based on multiple valves are identified. A confidence of the detection may be used to indicate confidence of calculated quantities and/or to place the sampling planes.

Cardiac flow detection based on morphological modeling in medical diagnostic ultrasound imaging

Systems and methods for occlusion detection in medical images are provided. An input medical image of one or more vessels in an anatomical object of a patient is received. One or more anatomical landmarks are identified in the input medical image. A first patch and one or more additional patches are extracted from the input medical image based on the identified one or more anatomical landmarks. The first patch and the one or more additional patches depict different portions of the anatomical object. Features are extracted from the first patch and the one or more additional patches using a machine learning based feature extractor network. An occlusion in the one or more vessels is detected in the first patch based on the extracted features with or without modeling features on a probability distribution function. Results of the detecting are output.

Large vessel occlusion detection and classification in medical imaging

For reconstruction in medical imaging, self-consistency using data augmentation is improved by including data consistency. Artificial intelligence is trained based on self-consistency and data consistency, allowing training without supervision. Fully sampled data and/or ground truth is not needed but may be used. The machine-trained model is less likely to reconstruct images with motion artifacts, and/or the training data may be more easily gathered by not requiring full sampling.

Self-supervised machine learning for medical image reconstruction

In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution. Combinations of one or more of uncertainty, use of synthetic training data, and therapy prediction may be provided.

Data-driven plaque determination in medical imaging

Systems and method for performing a medical imaging analysis task for making a clinical decision are provided. One or more input medical images of a patient are received. A medical imaging analysis task is performed from the one or more input medical images using a machine learning based network. The machine learning based network generates a probability score associated with the medical imaging analysis task. An uncertainty measure associated with the probability score is determined. A clinical decision is made based on the probability score and the uncertainty measure.

Machine learning for automatic detection of intracranial hemorrhages with uncertainty measures from medical images

Systems and methods for generating a probabilistic tree of vessels are provided. An input medical image of vessels of a patient is received. Anatomical landmarks are identified in the input medical image. A centerline of the vessels in the input medical image is determined based on the anatomical landmarks. A probabilistic tree of the vessels is generated based on a probability of fit of the anatomical landmarks and the centerline of the vessels. The probabilistic tree of the vessels is output.

Probabilistic tree tracing and large vessel occlusion detection in medical imaging

For magnetic resonance (MR) reconstruction using artificial intelligence (AI), the AI-based reconstruction for MR imaging systems is offloaded to one or more servers. A remote server performs AI-based reconstruction. A library of recent, old, custom, and/or publicly available AI-based reconstruction processes may be rapidly deployed and available to the server, which has the memory and processing resources for AI-based reconstruction. Load balancing of the data and/or between servers may improve performance.

Architecture for artificial intelligence-based magnetic resonance reconstruction

For autonomous MR scanning for a given medical test, a simplified MR scanner may be used without or will little input or control by a technologist (e.g., by a physician, radiologist, or person trained in MR scanner operation). The MR scanner autonomously positions, scans, checks quality, analyzes, and/or outputs an answer to a diagnostic question with or without an MR image. Scan analysis, based on artificial intelligence, allows for on-going or on-the-fly alteration of the scanning configuration to acquire the data desired to answer the diagnostic question. By using a simplified MR scanner, both position of the patient relative to the MR scanner and localization of the scan by the MR scanner are jointly solved. Sensors may sense a patient in a scan position where the reduced radio frequency requirements allow for a more open bore.

Autonomous magnetic resonance scanning for a given medical test

Systems and methods for training an artificial intelligence-based system using self-supervised learning are provided. For each respective training medical image of a set of unannotated training medical images, the following steps are performed. A first augmented image is generated by applying a first augmentation operation to the respective training medical image. A second augmented image is generated by applying a second augmentation operation to the respective training medical image. A first representation vector is created from the first augmented image using an encoder network. A second representation vector is created from the second augmented image using the encoder network. The first representation vector is mapped to first cluster codes. The second representation vector is mapped to second cluster codes. The encoder network is optimized using the first and second representation vectors and the first and second cluster codes.

Self-supervised learning for artificial intelligence-based systems for medical imaging analysis

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

CT scan parameters for performing a CT scan of an anatomical target region of a patient are determined and/or adjusted. An initial set of the CT scan parameters for starting to perform the CT scan is determined based on an initial set of attenuation curves associated with the anatomical target region of the patient. The initial set of attenuation curves are determined based on optical imaging data depicting the patient.

Determining CT scan parameters based on machine learning

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Princeton Junction, NJ, United States of America

Dorin Comaniciu