Siemens Healthcare Gmbh

Siemens Healthineers Ag

Siemens Aktiengesellschaft

Siemens Corporation

Siemens Aktiengesell Schaft

Michael Suehling

Michael Wels

Alexander Muehlberg

Grzegorz Soza

Shaohua Kevin Zhou

Michael Kelm

Dorin Comaniciu

David D Liu

Andreas Wimmer

Max Schoebinger

Martin Huber

Thomas Flohr

Tommaso Mansi

Sasa Grbic

Anna Jerebko

Adrian Barbu

Razvan Ionasec

Philipp Hoelzer

Chris Schwemmer

Christian Tietjen

Dijia Wu

Bogdan Georgescu

Yefeng Zheng

Bernhard Schmidt

Rui Liao

Shun P Miao

Christian Hofmann

Ingmar Voigt

Ute Feuerlein

Thilo Hannemann

Helene C Houle

Joerg Roland

Stefan Reichelt

Eva Rothgang

Thomas Pheiffer

Charles Henri Florin

Christopher Vincent Alvino

Jan Kretschmer

Martin Kramer

Alexander Katzmann

Oliver Taubmann

Felix Lades

Felix Durlak

Felix Denzinger

Rainer Kaergel

Sascha Seifert

Stefan Grosskopf

Maria Jimena Costa

Florian Thamm

Hendrik Ditt

Alexander Cavallaro

Leonhard Rist

Christian Petry

Michael Schrapp

Christian Hopfgartner

Matthias May

Ferdinand Distler

Pavlo Dyban

Helmut Ringl

Johannes Feulner

Christian Godenschwager

Stephen Ahmad

Frank Enders

Matthias Senn

Hannes Martinke

Dorothea Roth

Lisa Kratzke

Xun (Jason) Xu

Wladimir Lesyuk

Michael Uder

Stefanie Steding

Xinyun Li

A computer-implemented method is for providing radiomics-related information. In an embodiment, the computer-implemented method includes receiving radiomics-related data; determining, based on the radiomics-related data and an assistance algorithm, a function for processing the radiomics-related data; calculating, based on the radiomics-related data and the function for processing the radiomics-related data, the radiomics-related information; and providing the radiomics-related information.

Method and data processing system for providing explanatory radiomics-related information

One or more example embodiments of the invention relates in one aspect to a computer-implemented method for providing a stroke information. The method includes receiving examination data, the examination data comprising computed tomography imaging data of an examination area of a patient, the examination area of the patient comprising at least one brain region, the at least one brain region being affected by a stroke; adjusting a causal model based on the computed tomography imaging data to obtain an adjusted causal model, wherein the adjusted causal model models a first variable as a first cause for an appearance of the examination area of the patient; receiving a first value for the first variable; generating the stroke information based on the adjusted causal model and the first value for the first variable; and providing the stroke information.

Method and data processing system for providing a stroke information

A computer implemented method maps a three-dimensional branched tubular structure depicted in a three-dimensional image data set into at least one two-dimensional image plane.

Method, device and computer-readable medium for mapping a three-dimensional branched tubular structure into at least one two-dimensional image plane

A computer-implemented method for evaluating a three-dimensional angiography dataset of a blood vessel tree of a patient, comprises determining a variant information describing a belonging to at least one anatomical variant class of a plurality of anatomical variant classes relating to anatomical variants of the blood vessel tree based on a comparison of angiography information of the angiography dataset to reference information describing at least one of the anatomical variant classes.

Computer-implemented method for evaluating a three-dimensional angiography dataset, evaluation system, computer program and electronically readable storage medium

One or more example embodiments of the present invention relates in one aspect to a computer-implemented method includes receiving 2D topogram data of a patient; generating 2D topogram annotation data by applying a machine learning algorithm for topogram analysis onto the 2D topogram data; generating the medical imaging decision support data based on the 2D topogram annotation data; and providing the medical imaging decision support data.

Method for providing medical imaging decision support data and method for providing ground truth in 2D image space

A computer-implemented method for the further training of an artificial-intelligence evaluation algorithm that has already been trained based upon basic training data, wherein the evaluation algorithm ascertains output data describing evaluation results from input data comprising image data recorded with a respective medical imaging facility. In an embodiment, the method includes ascertaining at least one additional training data set containing training input data and training output data assigned thereto; and training the evaluation algorithm using the at least one additional training data set. The additional training data set is ascertained from the input data used during a clinical examination process with a medical imaging facility, which the already-trained evaluation algorithm was used, and output data of the already-trained evaluation algorithm that has been at least partially correctively modified by the user.

Computer-implemented method and system for training an evaluation algorithm, computer program and electronically readable data carrier

A computer-implemented method and system are for parametrizing a function including a processing algorithm and a representation generator, the representation generator being designed to generate at least one representation. In an embodiment, the method includes using an optimization algorithm to determine the processing algorithm and the at least one representation parametrization. The optimization algorithm optimizes a measure for the performance of the processing algorithm when operating on a set of training representations generated by applying the representation generator to training medical image datasets, by varying on the one hand the content of the at least one representation parametrization and/or the number of used representation parametrizations and on the other hand the processing algorithm and the algorithm parameters.

Computer-implemented method for parametrizing a function for evaluating a medical image dataset

A method, preferably a computer implemented method, and system are for providing output data associated with a medical imaging dataset of a patient, the medical imaging dataset including image data of a region of an anatomy of a patient including a plurality of coronary arteries. The output data is a significance score associated with a medical imaging data set of a patient. The method, in the most general terms, includes receiving input data, e.g. via a first interface, generating output data by applying algorithmic operations to the input data, and providing the output data, e.g. via a second interface.

Method and system for determining a significance score associated with a medical imaging dataset of a patient

A method is for providing a classification explanation. The method includes receiving an acquired medical image corresponding to an examination volume; and receiving or determining an actual classification value. The actual classification value classifies an anatomical structure within the acquired medical image. The method further includes receiving or determining a first classification value being different from the actual classification value, the first classification value being a potential classification value of the anatomical structure within the acquired medical image; applying a first generative function to the acquired medical image to generate a first synthetic image, the first generative function being based on the first classification value, and being configured to modify the anatomical structure to exhibit the first classification value; and providing a visualization based on the acquired medical image and the first synthetic image. Systems and computer programs are further for providing a classification explanation and/or generative functions.

Providing a classification explanation and a generative function

A computer-implemented method is for providing a clinical information. In an embodiment, the computer-implemented method includes receiving input data including a graph representation of a plurality of disease lesions of a patient; applying a trained function to the input data to generate the clinical information, the trained function being based on a graph machine learning model; and providing the clinical information, the clinical information including at least one information for prediction of the disease progression, survival or therapy response of the patient.

Method for obtaining disease-related clinical information

A method is for controlling a medical imaging system. The method includes providing a data record for a patient, the data record including indication data; mapping at least the indication data into an ontology; providing a control data library including control data records for at least one medical imaging system; automatically determining a control data record from the control data library as a function of the at least indication data mapped into the ontology, via at least one of an inference and numerical modeling; and controlling the medical imaging system with the control data record automatically determined. Furthermore, a related control facility and a medical imaging system are also disclosed.

Method and control facility for controlling a medical imaging system

A method is for supporting clinical decisions for a diagnosis or therapy of a patient using a medical imaging system. An embodiment of the method includes receiving a procedure order; based on the received procedure order, automatically identifying a clinical context of the ordered procedure; generating preliminary imaging data of at least a part of the patients anatomy; generating feature extraction data based on the identified clinical context and on the preliminary imaging data; extracting at least one clinical context specific feature using the generated feature extraction data; annotating the at least one extracted clinical context specific feature to obtain at least one annotated extracted feature; and selecting, for the identified clinical context, a similar case data set from a reference database of case data sets based on the at least one annotated extracted feature.

Method and system for supporting clinical decisions

An embodiment relates to a computer-implemented method for generating a combined tissue-vessel representation. The method includes receiving imaging data of a tissue; receiving imaging data of a vessel; generating a tissue representation based on the imaging data of the tissue; generating a vessel representation based on the imaging data of the vessel; and generating a combined tissue-vessel representation based on the vessel representation and the tissue representation, the vessel representation being overlaid over the tissue representation.

Method and computer system for generating a combined tissue-vessel representation

The method comprises receiving an image sequence of the subject from the camera during the medical imaging scan; receiving at least one of the current position or velocity of the patient table during the medical imaging scan; performing a motion tracking analysis of the image sequence to extract a motion model, wherein at least one of the motion tracking analysis or the motion model is tailored to the body region of interest and takes into account the at least one of the current patient table position or velocity; and analysing the motion model to detect subject motion and, if the detected motion is above a threshold, at least one of adapting the medical imaging examination or issuing an alert.

Method for controlling a medical imaging examination of a subject, medical imaging system and computer-readable data storage medium

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Erlangen, Germany

Michael Suehling