Koninklijke Philips Corporation N.v.

Koninklijkle Philips N.v.

Alexandra Groth

Juergen Weese

Rolf Jürgen Weese

Jochen Peters

Joerg Bredno

Axel Saalbach

Raoul Florent

Helko Lehmann

Matthias Bertram

Romane Isabelle Marie-Bernard Gauriau

Irina Waechter-Stehle

Frank Michael Weber

Kai Eck

Sabine Mollus

Vincent Maurice André Auvray

Hans Barschdorf

Stéphane Allaire

Rolf Jurgen Weese

Ronaldus Petrus Johannes Hermans

Tilman Wekel

Daniel Bystrov

Jens Von Berg

Yuechen Qian

Dirk Schaefer

Peter Maria Johannes Rongen

Jens Wiegert

Heidrun Steinhauser

Hannes Nickisch

Arne Ewald

Christoph Neukirchen

Kongkuo Lu

Vincent Maurice Andre Auvray

Ranjith Naveen Tellis

Tom Brosch

Heike Carolus

Georg Rose

Gundolf Kiefer

Jorg Bredno

Ran Cohen

Sven Kroenke

Irina Wachter-Stehle

Antonius Johannes Cornelius Bruijns

Michael Grass

Ivo Matteo Baltruschat

Qi Sun

Lior Wolloch

Bela Fadida

A system (SYS) for supporting a medical procedure, comprising an interface (IN) for receiving at least one medical input signal that describes a state of a target anatomy. A signal analyzer (SA) is configured to analyze the medical input signal to determine a time window for deployment of a cardio-vascular device (CL) to be deployed by a deployment.

Signal-triggered interventional device control

A prediction model is provided which is capable of predicting a correctness of a segmentation by a segmentation algorithm. The prediction model may be trained using a machine learning technique, and after training used to predict the correctness of a segmentation of a boundary in respective image portions of an image by the segmentation algorithm. The predicted correctness may then be visualized, for example as an overlay of the segmentation.

Predicting correctness of algorithmic segmentation

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

Multi-task deep learning method for a neural network for automatic pathology detection, comprising the steps: receiving first image data (I) for a first image recognition task; receiving (S2) second image data (V) for a second image recognition task; wherein the first image data (I) is of a first datatype and the second image data (V) is of a second datatype, different from the first datatype; determining (S3) first labeled image data (I) by labeling the first image data (I) and determining second synthesized labeled image data (I) by synthesizing and labeling the second image data (V); training (S4) the neural network based on the received first image data (I), the received second image data (V), the determined first labeled image data (I) and the determined second labeled synthesized image data (ISL); wherein the first image recognition task and the second image recognition task relate to a same anatomic region where the respective image data is taken from and/or relate to a same pathology to be recognized in the respective image data.

Multi-task deep learning method for a neural network for automatic pathology detection

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

A system and method are provided for use in evaluating a clinical guideline which is represented in a machine readable version by a decision tree comprising at least one node and a decision rule associated with the node. The decision rule comprises at least one variable representing a biomedical quantity. The biomedical quantity is extracted from the patient data using an ontology which defines concepts and their relationships in a medical domain of the clinical guideline and which thereby relates the variable of the decision rule to the patient data. If said extraction is not possible, a view of the patient data is presented to the user to enable the user to determine the biomedical quantity from the view. Advantageously, the user is assisted in evaluating the clinical guideline even when it is not possible to automatically extract the biomedical quantity from the patient data.

Evaluation of decision tree using ontology

An information retrieval system (IPS). The system comprises an input interface (IN) for receiving a query related to an object of interest. A concept mapper (CM) is configured to map the query to one or more associated concept entries of a hierarchic graph data structure (ONTO). The entries in said structure encode linguistic descriptors of components of a model (GM) for said object (OB). A metric-mapper (MM) is configured to map the query to one or more metric relationship descriptors. A geo-mapper (GEO) is configured to map said concept entries against the geometric model linked to the hierarchic graph data structure to obtain spatio-numerical data associated with said linguistic descriptors. A metric component (MTC) is configured to compute one or more metric or spatial relationships between said object components based on the spatio-numerical data and the one or more metric relationship descriptors.

Information retrieval apparatus

A system and method are provided for display of medical image data, with the display of the medical image data being determined on the basis of schematic image data of a schematic representation of an anatomical structure. The schematic representation may provide a particular view of the anatomical structure. The type of anatomical structure and the view of the anatomical structure provided by the schematic representation may be determined based on one or more image features in the schematic image data. The view may be characterized as a geometrically-defined perspective at which the anatomical structure is shown in the schematic representation. An output image may be generated showing the anatomical structure in the medical image data in accordance with said determined geometrically-defined perspective. A user may thus be provided with a display of medical image data which is easier to interpret having considered said schematic representation.

Display of medical image data

Image segmentation and prediction of segmentation

An apparatus provides a patient specific 3D model of a body part. At least one 2D X-ray image including 2D X-ray image data of a vascular structure of a patient's body part is provided. A 3D model of the body part is provided, the 3D model including a 3D modelled vascular structure. At least one parameter commands an appearance of the 3D modelled vascular structure. The 3D modelled vascular structure is compared with the 2D X-ray image data of the vascular structure to determine the at least one parameter. The 3D model is updated as a function of the determined at least one parameter. A medical report is generated based on information determined from the 3D model.

Three dimensional model of a body part

A system () for detecting and labeling structures of interest includes a current patient study database () containing a current patient study () with clinical contextual information (), a statistical model patient report database () containing at least one or more prior patient documents containing clinical contextual information (), an image metadata processing engine () configured to extract metadata for preparing an input for an anatomical structure classifier (), a natural language processing engine () configured to extract clinical context information () from the prior patient documents, an anatomical structure detection and labeling engine () or processor (), and a display device () configured to display findings from the current patient study. The anatomical structure detection and labeling engine () or processor () is configured to identify and label one or more structures of interest () from the extracted metadata and clinical context information () and aggregate series level data.

Optimized anatomical structure of interest labelling

There is provided a method and apparatus for segmenting two-dimensional images of an anatomical structure. A time sequence of two-dimensional images of the anatomical structure is acquired () and a segmentation model for the anatomical structure is acquired (). The segmentation model comprises a plurality of segments. The acquired segmentation model is applied to the entire time sequence of two-dimensional images of the anatomical structure simultaneously in time and space to segment the time sequence of two-dimensional images by way of the plurality of segments ().

Method and apparatus for segmenting two dimensional images of an anatomical structure

The present invention relates to a device for displaying image information, the device comprising: a detection unit (), which is configured to identify a plurality of admissible display orientations of multiple data sets; a restriction unit (), which is configured to restrict the plurality of admissible display orientations of at least one of the multiple data sets to a set of admissible display orientations in common for all the multiple data sets; and/or to restrict a plurality of admissible scrolling directions of at least one of the multiple data sets to a set of admissible scrolling directions that are normal to the restricted admissible display orientations; and a display unit (), which is configured to display the multiple data sets using the set of the restricted display orientations and/or the set of restricted scrolling directions.

Device and method for displaying image information

The present invention relates to an apparatus for providing a synthetic representation of a vascular structure. It is described to provide () at least one 2D X-ray image comprising 2D X-ray image data of a vascular structure of a patient's body part is provided. A 3D model of the body part is provided (), the 3D model comprising a 3D modelled vascular structure. A 2D projection of the 3D model of the body part is determined (), the 2D projection of the 3D model of the body part comprising a 2D projection of the 3D modelled vascular structure. The 3D model of the body part is transformed (). The transform of the 3D model of the body part comprises a determination of the pose of the 3D model of the body part such that a 2D projection of the 3D modelled vascular structure associated with the transformed 3D model of the body part is representative of the 2D X-ray image data of the vascular structure of the patient's body part. A medical report is generated () based on information determined from the 3D model.

Synthetic representation of a vascular structure

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Hamburg, Germany

Alexandra Groth