Koninklijke Philips Corporation N.v.

Nuance Communications Austria Gmbh

U.S. Philips Corporation

Nuance Communications, Inc.

Koninklijke Philips Electronics N.v.

Philips Intellectual Property & Standards Gmbh

Jochen Peters

Juergen Weese

Frank Michael Weber

Rolf Jürgen Weese

Carsten Meyer

Irina Waechter-Stehle

Alexandra Groth

Arne Ewald

Dietrich Klakow

Thomas Heiko Stehle

Tobias Wissel

Evgeny Matusov

Martin Bergtholdt

Lyubomir Georgiev Zagorchev

Matthias Lenga

Olivier Ecabert

Heinrich Schulz

Fabian Wenzel

Christian Buerger

Nicole Schadewaldt

Reinhard Kneser

Tom Brosch

Alexander Schmidt-Richberg

Irina Wachter-Stehle

Daniel Bystrov

Jens Von Berg

Helko Lehmann

Torbjorn Vik

André Goossen, Sr

Sebastian Wild

Rafael Wiemker

Steffen Weiss

Tobias Klinder

Cristian Lorenz

Tim Nielsen

Liran Goshen

Yuechen Qian

Amir Mohammad Tahmasebi Maraghoosh

Eberhard Sebastian Hansis

Steffen Renisch

Claas Bontus

Axel Saalbach

Christian Lorenz

Sabine Mollus

Karsten Sommer

Hannes Nickisch

Harald Sepp Heese

Eric Thelen

Scott Holland Settlemier

Heike Carolus

Dieter Geller

Bernd Hofmann

Rolf Juergen Weese

Gundolf Kiefer

Rolf Jurgen Weese

Torbjørn Vik

Elizabeth Anne Morre

Matthew A Garlinghouse

Robert M Roth

Thomas W McAllister

Ali Majidi

Christophe Michael Jean Schülke

Hauke Schramm

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

The present invention relates to multispectral imaging. In order to improve an identification of relevant multispectral material transitions (in particular caused by injected contrast agent), an apparatus is proposed to use the local maxima of the variances and/or covariances of the intensities of the multi-channel images to locate material transitions. In comparison to gradient vectors, the local variance is not directed and not prone to noise. An alternative apparatus is proposed to use the local covariance deficits of the intensities of the multi-channel images to locate material transitions. The proposed alternative approach is independent of spatial drifts across the image volume.

Local spectral-covariance or local spectral covariance deficits computation and display for highlighting of relevant material transitions in spectral CT and MR

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

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 relates to a system and computer-implemented method for enabling correction of a segmentation of an anatomical structure in 3D image data. The segmentation may be provided by a mesh which is applied to the 3D image data to segment the anatomical structure. The correction may for example involve a user directly or indirectly selecting a mesh part, such as a mesh point, that needs to be corrected. The behaviour of the correction, e.g., in terms of direction, radius/neighbourhood or strength, may then be dependent on the mesh normal direction, and in some embodiments, on a difference between the mesh normal direction and the orientation of the viewing plane.

Mesh correction depending on mesh normal direction

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

A system for establishing a contour of a structure is disclosed. An initialization subsystem () is used for initializing an adaptive mesh representing an approximate contour of the structure, the structure being represented at least partly by a first image, and the structure being represented at least partly by a second image. A deforming subsystem () is used for deforming the adaptive mesh, based on feature information of the first image and feature information of the second image. The deforming subsystem comprises a force-establishing subsystem () for establishing a force acting on at least part of the adaptive mesh, in dependence on the feature information of the first image and the feature information of the second image. A transform-establishing subsystem () is used for establishing a coordinate transform reflecting a registration mismatch between the first image, the second image, and the adaptive mesh.

Establishing a contour of a structure based on image information

A system and computer-implemented method are provided for preprocessing medical image data for machine learning. Image data is accessed which comprises an anatomical structure. The anatomical structure in the image data is segmented to obtain a segmentation of the anatomical structure as a delineated part of the image data. A grid is assigned to the delineated part of the image data, the grid representing a partitioning of an exterior and interior of the type of anatomical structure using grid lines, wherein said assigning comprises adapting the grid to fit the segmentation of the anatomical structure in the image data. A machine learning algorithm is then provided with an addressing to the image data in the delineated part on the basis of coordinates in the assigned grid. In some embodiments, the image data of the anatomical structure may be resampled using the assigned grid. Advantageous, a standardized addressing to the image data of the anatomical structure is provided, which may reduce the computational overhead of the machine learning, require fewer training data, etc.

Preprocessing medical image data for machine learning

Systems and methods are provided for generating and using statistical data which is indicative of a difference in shape of a type of anatomical structure between images acquired by a first imaging modality and images acquired by a second imaging modality. This statistical data may then be used to modify a first segmentation of the anatomical structure which is obtained from an image acquired by the first imaging modality so as to predict the shape of the anatomical structure in the second imaging modality, or in general, to generate a second segmentation of the anatomical structure as it may appear in the second imaging modality based on the statistical data and the first segmentation.

Generating shape difference data between multiple imaging modalities

An ultrasound imaging system for acquiring ultrasound images of an anatomical feature of interest in a subject, comprising a controller operable by a user and configured to: process input ultrasound images to extract anatomical data; determine a set of constraints to be applied to the ultrasound images, the constraints being spatial, temporal and/or of image quality, derived from the extracted anatomical data and/or on user input; monitor the ultrasound images, as they are received, for determining their compliance with the determined constraints; and output an indication based on the determined compliance. The user can adapt the imaging process using the feedback of these indications, and can decide to stop the process based on satisfactory indications.

Ultrasound imaging system and method

A system () and computer-implemented method are provided for data collection for distributed machine learning of a machine learnable model. A privacy policy data () is provided defining computer-readable criteria for limiting a selection of medical image data () to a subset of the medical image data to obfuscate an identity of the at least one patient. The medical image data is selected based on the computer-readable criteria to obtain privacy policy-compliant training data () for transmission to another entity. The system and method enable medical data collection at clinical sites without requiring manual oversight, and enables such selections to be made automatically, e.g., based on a request for medical image data which may be received from outside of the clinical site.

Medical data collection for machine learning

An ultrasound imaging system comprises a display for displaying a received ultrasound image. A user interface is provided for receiving user commands for controlling the ultrasound imaging process, and it receives a user input which identifies a point or region of the displayed ultrasound image. An image depth is determined which is associated with the identified point or region and the imaging process is controlled to tailor the imaging to the identified point or region.

Ultrasound imaging system and method for deriving depth and identifying anatomical features associated with user identified point or region

Image segmentation and prediction of segmentation

The invention relates to a segmentation system for segmenting an object in an image. The segmentation system is configured to place a surface model comprising surface elements within the image, to determine for each surface element a respective sub volume () of the image and to use a neural network () for determining respective distances between the surface elements and the boundary of the object in the image based on the determined subvolumes. The surface model is then adapted in accordance with the determined distances, in order to segment the object. This segmentation, which is based on the subvolumes of the image and the neural network, is improved in comparison to known techniques which rely, () for instance, on a sampling of candidate points along lines being perpendicular to the respective surface element and on a determination of likelihoods for the candidate points that they indicate a boundary of the object.

Segmentation system for segmenting an object in an image

There is provided a method and apparatus for refining a model of an anatomical structure in an image. A model for the anatomical structure in the image is acquired. The model comprises a plurality of control points, each control point corresponding to a feature in the anatomical structure. The model is placed in the image with respect to the anatomical structure. Based on a user input received to adjust the model in the image, a position of at least one of the plurality of control points is adjusted to alter a shape of the model to the anatomical structure in the image, wherein adjustment of the position of one or more of the at least one control points is restricted based on information relating to the at least one control point.

Method and apparatus for refining a model of an anatomical structure in an image

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Norderstedt, Germany

Jochen Peters