Koninklijke Philips Corporation N.v.

Elekta, Inc.

Rolf Jürgen Weese

Jochen Peters

Alexandra Groth

Alfonso Agatino Isola

Christoph Neukirchen

Irina Waechter-Stehle

Frank Michael Weber

Heinrich Schulz

Steffen Renisch

Sven Kabus

Torbjoern Vik

Arne Ewald

Harald Sepp Heese

Tom Brosch

Hrishikesh Narayanrao Deshpande

Maria Luiza Bondar

Stéphane Allaire

Rafael Wiemker

Tim Nielsen

Liran Goshen

Dirk Schaefer

Martin Bergtholdt

Axel Saalbach

Claas Bontus

Jens Wiegert

Karsten Sommer

Hannes Nickisch

Alexander Schmidt-Richberg

Heike Carolus

Matthieu Frédéric Bal

Sven Kroenke

Tilman Wekel

Christophe Michael Jean Schülke

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 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

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 relates to a system for determining a radiation therapy plan for a radiation therapy system (), comprising a multi-leaf collimator. The radiation therapy plan determination system () comprises a therapy system characteristics providing unit (), wherein the characteristics comprise possible leaf positions and possible radiation fluence values, a planning objectives providing unit (), wherein the planning objectives are indicative of a desired therapeutic radiation dose distribution, an optimization function providing unit (), wherein the optimization function is indicative of a deviation of a radiation dose distribution from the planning objectives and of an uncertainty of the radiation dose distribution at edges of the possible apertures, and a therapy plan optimization unit () adapted to determine a sequence of possible apertures and possible radiation fluence values for which the optimization function is optimized. Thus, an optimal therapy plan can be provided for each individual patient.

System, method and computer program for determining a radiation therapy plan for a radiation therapy system

A device for optimizing a radiation therapy plan () for delivering therapeutic radiation to a patient using a therapeutic radiation source () while modulated by a multi-leaf collimator (MLC) () includes at least one electronic processor () connected to a radiation therapy device (). A non-transitory computer readable medium () stores instructions readable and executable by the at least one electronic processor to perform a radiation therapy plan optimization method () including: optimizing MLC settings of the MLC respective to an objective function wherein the MLC settings define MLC leaf tip positions for a plurality of rows of MLC leaf pairs at a plurality of control points (CPs). The optimizing is performed in two or more iterations with a resolution of the MLC settings increasing in successive iterations.

Fast generation of multi-leaf collimator (MLC) openings using hierarchical multi-resolution matching

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

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

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

The invention relates to a system for assisting in evaluating a contour of an anatomic structure () with respect to a dose distribution corresponding to a treatment plan for a radiation therapy treatment of a patient. The system comprises an evaluation unit particularly configured to evaluate the dose distribution in varying distances from the contour of the anatomic structure () to determine at least one point where the evaluated dose distribution fulfills a predetermined condition, and to determine the distance between the at least one point and the contour and/or to visualize the at least one point to a user of the system.

Evaluation of an anatomic structure with respect to a dose distribution in radiation therapy planning

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

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

A method and apparatus for segmenting a two-dimensional image of an anatomical structure includes acquiring () a three-dimensional model of the anatomical structure. The three-dimensional model includes a plurality of segments. The acquired three-dimensional model is adapted to align the acquired three-dimensional model with the two-dimensional image (). The two-dimensional image is segmented by the plurality of segments of the adapted three-dimensional model.

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Norderstedt, Germany

Rolf Jürgen Weese