Koninklijke Philips Corporation N.V.

André Goossen, Sr

Jochen Peters

Irina Waechter-Stehle

Frank Michael Weber

Arne Ewald

Matthias Lenga

Sebastian Wild

Rafael Wiemker

Daniel Bystrov

Jens Von Berg

Stewart Young

Axel Saalbach

Tim Philipp Harder

Tobias Wissel

Tom Brosch

Scott Holland Settlemier

Hrishikesh Narayanrao Deshpande

Sven Kroenke

Heiner Matthias Brueck

A computer implemented method of making a measurement associated with a feature of interest in an image. The method comprises using () a model trained using a machine learning process to take the image as input and predict a pair of points between which to make the measurement of the feature of interest in the image. The method then comprises determining () the measurement, based on the predicted pair of points.

Making measurements in images

The invention provides a method for determining a confidence value for an image segmentation. The method includes obtaining an image, wherein the image comprises a view of an anatomical structure and a model of the anatomical structure is obtained, wherein the model comprises a plurality of nodes. The image is processed to generate a plurality of image segmentation outputs, wherein each image segmentation output comprises a set of values for the view, wherein each value of the set of values is associated with a node of the plurality of nodes of the model. For each node of the model, a confidence value is determined based on the plurality of values corresponding to the node. A confidence map of the anatomical structure is generated based on the confidence value of each node.

Systems and methods for medical imaging

A computer-implemented method for positioning a subject in medical imaging, comprising: receiving a first image () of a region of interest () of the subject (S); determining first positioning data based on the first image (), wherein the first positioning data indicates an alignment of the region of interest () relative to a first image acquisition unit used to acquire the first image () (S); determining guidance data based on the first positioning data, wherein the guidance data comprises a guidance for an alignment of the region of interest () relative to a second image acquisition unit used to acquire a second image () from a current alignment to a target alignment, wherein the target alignment is to correspond to that derived from the first positioning data (S); providing the guidance data for acquiring the second image () (S).

Guidance for positioning a patient in medical imaging

The present invention relates to a method and system for predicting X-ray degradation, the system comprising; a generator () configured to generate a deployment fingerprint data set for recording cumulative radiation exposure of a currently deployed X-ray tube; a database () configured to provide a training data set comprising multiple tube fingerprint data sets for recording cumulative radiation exposure of previously deployed X-ray tubes correlated with failures of the previously deployed X-ray tubes; and a neural network () configured to be trained using the training data set and configured to predict at least one parameter of the currently deployed X-ray tube based on the training.

Predicting tube degradation via filament or exposure fingerprints using neural networks

The invention provides a method for refining a mapped surface mesh of a cardiac chamber. The method includes obtaining a mapped surface mesh of the cardiac chamber anatomy, wherein the mapped surface mesh comprises a central region representing a cardiac chamber and an outer region representing a peripheral cardiac structure connected to the cardiac chamber, and wherein the mapped surface mesh comprises a first view of an anatomical landmark within the cardiac chamber, and obtaining image data of a cardiac chamber anatomy of a subject. The central region of the mapped surface mesh is deformed based on a first segmentation algorithm configured according to one or more predetermined shape-constraints and the outer region of the mapped surface mesh is deformed based on a second segmentation algorithm configured according to the image data, thereby generating a deformed outer region. The deformed central region and the deformed outer region are then combined, thereby generating a refined mapped surface mesh.

Methods and systems for cardiac chamber imaging

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Munich, Georgia

André Goossen, Sr