Siemens Healthineers Ag

Siemens Healthcare Gmbh

Serkan Cimen

Mehmet Akif Gulsun

Puneet Sharma

Michael Wels

Chris Schwemmer

Peter Mountney

Max Schoebinger

Pascal Ceccaldi

Felix Lades

Christian Hopfgartner

Rumman Khan

Suha Ayman

Ashish Jaiswal

Tiziano Passerini

Dominik Neumann

Martin Berger

Daniel Toth

Martin Von Roden

Angiography angles are determined. Patient information and target vessel information are obtained, wherein the patient information defines individual medical information of a patient and wherein the target vessel information defines at least one target vessel to be imaged. At least one angiography angle is determined based on the patient information and the target vessel information. Angiograms obtained using the at least one angiography angle are analyzed to determine a vessel coverage of the target vessel and based on the vessel coverage determines additional angiography angles.

Method and system for improved vessel coverage from angiography

At least one example embodiment provides a computer-implemented method for evaluating at least one image data set of an imaging region of a patient, wherein at least one evaluation information describing at least one medical condition in an anatomical structure of the imaging region is determined.

Computer-implemented methods and evaluation systems for evaluating at least one image data set of an imaging region of a patient, computer programs and electronically readable storage mediums

Systems and methods for determining corresponding locations of points of interest in a plurality of input medical images are provided. A plurality of input medical images comprising a first input medical image and one or more additional input medical images is received. The first input medical image identifies a location of a point of interest. A set of features is extracted from each of the plurality of input medical images. Features between each of the sets of features are related using a machine learning based relational network. A location of the point of interest in each of the one or more additional input medical images that corresponds to the location of the point of interest in the first input medical image is identified based on the related features. The location of the point of interest in each of the one or more additional input medical images is output.

Multi-view matching across coronary angiogram images

Method and system for image registration or image segmentation. The method includes receiving an image which is to be processed by a first machine-learning model to perform, for example, image registration or segmentation, and using a second machine-learning model to determine if the received image is of a quality suitable for the first machine-learning model to act upon.

Method and system for image analysis

In a system and method for analyzing images, an input image is provided to a computer and is processed therein with a first deep learning model so as to generate an output result for the input image; and applying a second deep learning model is applied to the input image to generate an output confidence score that is indicative of the reliability of any output result from the first deep learning model for the input image.

Method and system for generating a confidence score using deep learning model

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Jersey City, NJ, United States of America

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