Koninklijke Philips Corporation N.v.

Matthias Lenga

Jochen Peters

Irina Waechter-Stehle

Arne Ewald

Tobias Wissel

Frank Michael Weber

Tobias Klinder

André Goossen, Sr

Sebastian Wild

Rafael Wiemker

Cristian Lorenz

David N Roundhill

Steffen Renisch

Christian Buerger

Alexander Schmidt-Richberg

Scott Holland Settlemier

Heike Carolus

Rolf Juergen Weese

Eliza Teodora Orasanu

Marten Bergtholdt

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

In a method of segmenting a feature in an image, an image product related to the image is provided () to a model trained using a machine learning process. An indication of a shape descriptor for the feature in the image is received () from the model, based on the image product. The indicated shape descriptor is then used () in a model based segmentation, MBS, to initialize the MBS and segment the feature.

Methods and systems for segmenting images

Some embodiments are directed to a segmentation of medical images. For example, a medical image may be registering to multiple atlas images after which a segmentation function may be applied. Multiple segmentation may be fused into a final overall segmentation. The atlas images may be selected on the basis of high segmentation quality or low registration quality.

Medical image segmentation and atlas image selection

A mechanism for creating/synthesizing realistic training data, for training a machine-learning model, using anatomical knowledge. An anatomical model can be obtained. Information from annotated training data entries (i.e. 'ground truth' information), can be used to model the anatomical variation, from the obtained model, in the population of the training data. This anatomical model can then be modified, e.g. incorporating some random factors, in order to generate one or more augmented models of realistic anatomies. The augmented anatomy is then transferred from the model domain to the data entry domain to thereby generate a new data entry or data entries for training a machine-learning model. This latter process can be achieved in various ways, e.g. using GANs, such as CycleGANs and label images, or deformation vector fields.

Data augmentation for a machine learning method

An ultrasound imaging system includes a processor circuit in communication with an ultrasound transducer configured to receive three-dimensional ultrasound data of an anatomy, and generate a target image corresponding to a target image plane of the anatomy, and a plurality of adjacent images corresponding to image planes adjacent to the target image plane along a simulated motion path. The processor circuit is further configured to display the target image, receive a user input representative of a direction of motion along the simulated motion path, and display an adjacent image of the plurality of adjacent images corresponding to the direction of motion. Accordingly, the user can observe the target image in its spatial context by scanning through the target image and one or more adjacent images on the display as if the ultrasound transducer were being scanned along the simulated motion path.

Contextual multiplanar reconstruction of three-dimensional ultrasound imaging data and associated devices, systems, and methods

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

The invention provides for a method for switching between fields of view of an ultrasound probe. The method begins by obtaining an anatomical model representing a region of interest of a subject and establishing a first field of view relative to an ultrasonic probe, wherein the first field of view comprises an initial portion of the region of interest. Ultrasound data is then obtained from the first field of view by way of the ultrasonic probe and a first anatomical feature is identified within the first field of view based on the ultrasound data. A location in digital space of the first field of view relative to the anatomical model is determined based on the first anatomical feature. A second field of view is then established based on the anatomical model and the first field of view, wherein the first field of view functions as a reference field of view. The field of view is then switched from the first field of view to the second field of view.

Methods and systems for adjusting the field of view of an ultrasound probe

Presented are concepts for obtaining a confidence measure for a machine learning model. One such concept process input data with the machine learning model to generate a primary result. It also generate a plurality of modified instances of the input data and processes the plurality of modified instances of the input data with the machine learning model to generate a respective plurality of secondary results. A confidence measure relating to the primary result is determined based on the secondary results.

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MAINZ, Germany

Matthias Lenga