Siemens Healthcare Gmbh

Siemens Healthineers Ag

Siemens Aktiengesellschaft

Siemens Medical Solutions Usa, Inc.

New York University

Sasa Grbic

Dorin Comaniciu

Bogdan Georgescu

Zhoubing Xu

Tommaso Mansi

Siqi Liu

Razvan Ionasec

Ali Kamen

Matthias John

Ingmar Voigt

Arnaud Arindra Adiyoso

Shaohua Kevin Zhou

Jan Boese

Daguang Xu

Razvan Ioan Ionasec

Dominik Neumann

Julian Krebs

Florin-Cristian Ghesu

Guillaume Chabin

Rui Liao

Eli Gibson

Florin Cristian Ghesu

Awais Mansoor

Yefeng Zheng

Vivek Kumar Singh

Bernhard Geiger

Li Zhang

Shun P Miao

Helene C Houle

Philipp Hoelzer

Jin-hyeong Park

Peter Mountney

Charles Henri Florin

Esther Raithel

Thomas Re

Sebastian Guendel

Pierre De Tournemire

Dana Lin

Shikha Chaganti

Puneet Sharma

Michael Suehling

Fernando Vega-Higuera

Dominik Bernhardt

Yang Wang

Youngjin Yoo

Bruce S Spottiswoode

Brian Teixeira

Andrzej Milkowski

Sebastien Piat

Puyang Wang

Michael Schwier

Terrence Chen

Thomas Flohr

Mariappan S Nadar

Boris Mailhe

Thomas Mertelmeier

Benjamin L Odry

David D Liu

Ankur Kapoor

Matthias Fenchel

Steffen Kappler

Grzegorz Soza

Stefan Kluckner

Estelle Camus

Anna Jerebko

Viorel Mihalef

Bin Lou

Noha Youssry El-Zehiry

Robert Grimm

Ludwig Ritschl

Chris Schwemmer

Olivier Pauly

Max Schoebinger

Thomas Pheiffer

Mingqing Chen

Sebastian Vogt

Jan Kretschmer

Young-Ho Kim

Dime Vitanovski

Heinrich Von Busch

Ruhan Sa

Julia Wicklein

Ingo Schmuecking

Jonathan Sperl

Max Schöbinger

Günther Platsch

Fernando Vega

Ren-Yi Lo

Abdoul Aziz Amadou

Tae Soo Kim

Philipp Hölzer

Fei Gao

He Zhang

Vlad Comanelea-Serban

Marvin Teichmann

Amitkumar Bhupendrakumar Shah

Arnaud Gaillard-Moguilny

Yuankai Huo

Systems and methods for automatically navigating a catheter in a patient are provided. An image of a current view of a catheter in a patient is received. A set of actions of a robotic navigation system for navigating the catheter from the current view towards a target view is determined using a machine learning based network. The catheter is automatically navigated in the patient from the current view towards the target view using the robotic navigation system based on the set of actions.

Smart image navigation for intracardiac echocardiography

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

Techniques of determining a quantification of at least one characteristic of a muscle structure comprising at least one muscle and at least one tendon are disclosed. The quantification of the at least one characteristic of the rotator cuff may be determined by using at least one artificial neural network and based on one or more medical images depicting the muscle structure of a patient.

Determining characteristics of muscle structures using artificial neural network

Systems and methods for automatically staging non-small cell lung cancer are provided. Patient data relating to a cancer of a patient is received. The patient data comprises one or more medical images and one or more biopsy reports. A T-stage of the cancer is determined based on a location and a size of one or more tumors of the cancer determined using the patient data. An N-stage of the cancer is determined by combining a metastasis evaluation of the cancer to regional lymph nodes determined from the one or more medical images and a metastasis evaluation of the cancer in the regional lymph nodes determined from the one or more biopsy reports. An M-stage of the cancer is determined based on a metastasis evaluation of the cancer to anatomical structures based on the patient data. The T-stage, the N-stage, and the M-stage are output.

Automatic staging of non-small cell lung cancer from medical imaging and biopsy reports

For supporting reading for radiologists, artificial intelligence (AI) identifies normal anatomy or parts of the patient represented in a medical image. Those parts are then altered, such as redacted, to indicate that no review of that part is needed. The reading presents medical images for review with one or more parts identified as not needing review (e.g., by being missing through redaction).

Artificial intelligence supported reading by redacting of a normal area in a medical image

A computer-implemented method is for providing quantitative airway information. In an embodiment, the method includes receiving and/or determining first medical image data of an airway segment; applying a first trained function to the first medical image data, to generate output data; determining the at least one quantitative airway information of the airway segment based on the output data; and providing the at least one quantitative airway information.

Method for providing airway information

Systems and methods for performing an assessment of a lesion are provided. A plurality of input medical images of a lesion is received. The plurality of input medical images comprises an initial input medical image and one or more additional input medical images. The initial input medical image comprises a region of interest around the lesion. A mask of the lesion is curated for the initial input medical image based on the region of interest and a set of candidate masks. The region of interest in the initial input medical image is propagated to the one or more additional input medical images based on prior registration transformations. A mask of the lesion is curated for each of the one or more additional input medical images based on the propagated regions of interest and the set of candidate masks. One or more assessments of the lesion are performed based on the mask for the initial input medical image, the masks for the one or more additional input medical images, and prior assessments of lesions. Results of the one or more assessments of the lesion are output.

AI driven longitudinal liver focal lesion analysis

For robust view classification and measurement estimation in sequential ultrasound imaging, the classification and/or measurements for a given image or sequence of images are gated. To prevent oscillation in results, the gating provides consistent output.

Robust view classification and measurement in ultrasound imaging

The invention describes a method for automatically localizing organ segments in a three-dimensional image comprising the following steps: providing a three-dimensional image showing at least one organ and at least one tubular network comprising a plurality of tubular structures, the organ comprising organ segments; performing automatic separation of the organ from other parts of the image; performing automatic tracing of the tubular network to obtain a branch map; performing automatic analysis of the branch map to identify specific tubular structures; performing automatically assigning regions of the organ to the specific tubular structures to segment the organ into localized organ segments; and outputting the localized organ segments and the traced and analyzed tubular network as image data. The invention further describes a localization arrangement and a medical imaging system.

Method and arrangement for automatically localizing organ segments in a three-dimensional image

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

Systems and methods for an intuitive display of one or more anatomical objects are provided. One or more 3D medical images of one or more anatomical objects of a patient are received. Correspondences between the one or more 3D medical images and points on a 2D map representing the one or more anatomical objects are determined. The 2D map is updated with patient information extracted from the one or more 3D medical images. The updated 2D map with the determined correspondences is output.

Intuitive display for rotator cuff tear diagnostics

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

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

Growing community of inventors

Plainsboro, NJ, United States of America

Sasa Grbic