Koninklijke Philips Corporation N.v.

Tobias Klinder

Rafael Wiemker

Cristian Lorenz

Sven Kabus

Michael Grass

Eberhard Sebastian Hansis

Martin Bergtholdt

Alexander Schmidt-Richberg

Irina Waechter-Stehle

Jens Von Berg

Holger Schmitt

Christian Haase

Nicole Schadewaldt

Raymond C Chan

Thomas Buelow

Robert Manzke

Dirk Schaefer

Steffen Renisch

Hanno Heyke Homann

Axel Saalbach

Astrid Ruth Franz

Hans Barschdorf

Bharat Ramachandran

Christian Buerger

Thomas Blaffert

Jörg Sabczynski

Tim Philipp Harder

Heike Carolus

Matthias Lenga

Thomas Koehler

Daniel Bystrov

Jochen Peters

David N Roundhill

Michael Harald Kuhn

Liran Goshen

Frank Michael Weber

Xiyun Song

Udo Van Stevendaal

Karsten Sommer

Joerg Sabczynski

Hannes Nickisch

Peter Bornert

Dirk Schäfer

Laurence Rouet

Kongkuo Lu

Sheng Xu

Tom Brosch

Ingwer Curt Carlsen

Sebastian Peter Michael Dries

Torbjorn Vik

Tanja Nordhoff

Roland Johannes Opfer

Caroline Denise Francoise Raynaud

Cybèle Ciofolo-Veit

Thierry Lefevre

Heike Ruppertshofen

Cherif Sahyoun

Ming De Lin

Eliza Teodora Orasanu

Raghed Hanna

Robin M B Wolz

Robin Mitja Benjamin Wolz

Marten Bergtholdt

Mukta Joshi

The invention refers to an apparatus () for generating an augmented image comprising a) a base image providing unit (), wherein the base image is generated based on a combination of spectral image data, b) a contrast image providing unit (), wherein the contrast image is generated based on a different combination of the spectral image data, c) a degree of saliency determination unit (), wherein the degree of saliency is indicative of a difference between an image value of a voxel of the contrast image and an image value of a corresponding voxel of a predetermined template image, and d) an augmented image generation unit () for generating an augmented base image of the object by augmenting voxels of the base image based on the degree of saliency. The invention allows to provide the augmented base image with an improved image quality and information content.

Apparatus for generating an augmented image of an object

A three-dimensional virtual endoscopy rendering of a lumen of a tubular structure is based on both non-spectral and spectral volumetric imaging data. The three-dimensional virtual endoscopy rendering includes a 2-D image of a lumen of the tubular structure from a viewpoint of a virtual camera of a virtual endoscope passing through the lumen. In one instance, the three-dimensional virtual endoscopy rendering is similar to the view which is provided by a physical endoscopic video camera inserted into the actual tubular structure and positioned at that location. The non-spectral volumetric image data is used to determine an opacity and shading of the three-dimensional virtual endoscopy rendering. The spectral volumetric image data is used to visually encode the three-dimensional virtual endoscopy rendering to visually distinguish an inner wall of the tubular structure and structure of interest on the wall.

3-D virtual endoscopy rendering

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 guiding the acquisition of an ultrasound image. A 3D ultrasound image is acquired by an ultrasound probe at a first position and an anatomical structure is identified within the 3D ultrasound image. A target imaging plane is estimated based on the identified anatomical structure and it is determined whether the target imaging plane is present within the 3D ultrasound image. If the target imaging plane is present, a displacement between a central plane of the 3D ultrasound image and the target plane is determined. If the displacement is below a predetermined threshold, the target imaging plane is extracted and if the displacement is above the predetermined threshold, an instruction to acquire a 3D ultrasound image with the ultrasound probe at a second position, different from the first position, is generated based on the displacement. The invention further provides a method for estimating a target imaging plane. A central plane of a 3D ultrasound image is obtained, wherein the central plane contains at least part of an anatomical structure and abounding box is generated around the anatomical structure. The bounding box is divided into a grid having a plurality of grid points and, for each grid point; an offset is estimated between the central plane and the target imaging plane. Finally, coordinates of the target imaging plane are estimated based on the offset for each grid point of the bounding box.

Systems and methods for guiding the acquisition of an ultrasound image

A method and system for mapping boundary detecting features of at least one source triangulated mesh of known topology to a target triangulated mesh of arbitrary topology. A region of interest in a volumetric image associated with each triangle of the target triangulated mesh is provided to a feature mapping network. The feature mapping network assigns a feature selection vector to each triangle of the target triangulated mesh. The associated region of interest and assigned feature selection vector for each triangle of the target triangulated mesh are provided to a boundary detection network. A predicted boundary based on features of the associated region of interest selected by the assigned feature selection vector is obtained from the boundary detection network.

Model-based image segmentation

The invention provides a method for making anatomical measurements using a captured ultrasound image representative of the anatomical region of interest. The method comprises receiving () data representative of a set of points selected by a user within the image and interpreting from the points a particular anatomical measurement type which the user is intending to perform. In particular, the points are processed to identify () a pattern or other geometrical characteristic of the points, and the location () of at least one of the points within the image is also identified. These two characteristics are used to identify from the set of points which measurement the operator is performing. Once the measurement is identified, an appropriate measurement template () is selected and applied in order to derive () from the set of points an anatomical measurement.

Device and method for obtaining anatomical measurements from an ultrasound image

A system for generating ultrasound data in respect of an anatomical body being scanned makes use of a predetermined scan protocol, which specifies a sequence of types of ultrasound scan of structures of interest. These types may for example be different imaging modalities (static, dynamic, 2D, 3D etc.) which are most appropriate for viewing different structures within the anatomical body. From received ultrasound images, a model is used to identify the structures of interest within the ultrasound images. The best images for creating the types of scan of the protocol are then identified and a sequence is compiled of those best images. In this way, a sequence is created which combines different types of scan, in a structured way according to a predetermined protocol. This makes the analysis of the sequence most intuitive for a user, and simplifies comparison between different sequences.

Ultrasound imaging system and method

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.

Confidence measure for a deployed machine learning model

The invention relates to a system for assessing a pulmonary image which allows for an improved assessment with respect to lung nodules detectability. The pulmonary image is smoothed for providing different pulmonary images (20, 21, 22) with different degrees of smoothing, wherein signal values and noise values, which are indicative of the lung vessel detectability and the noise in these images, are determined and used for determining an image quality being indicative of the usability of the pulmonary image to be assessed for detecting lung nodules. Since a pulmonary image shows lung vessels with many different vessel sizes and with many different image values, which cover the respective ranges of potential lung nodules generally very well, the image quality determination based on the different pulmonary images with different degrees of smoothing allows for a reliable assessment of the pulmonary image's usability for detecting lung nodules. The image quality is used to determine a radiation dose level to be applied for generating a next pulmonary image.

System and method for assessing a pulmonary image

The invention provides for a medical imaging system () comprising: a memory () for storing machine executable instructions (), a display () for rendering a user interface (), and a processor (). Execution of the machine executable instructions causes the processor to receive () three dimensional medical image data () descriptive of a region of interest () of a subject (). The region of interest comprises a spine (). Execution of the machine executable instructions further causes the processor to receive () a set of spinal coordinate systems () each descriptive of a location and an orientation of spinal vertebrae in the three dimensional medical image data. The set of spinal coordinate systems further comprises a set of spine centerline positions () each positioned on a spine centerline (). Execution of the machine executable instructions further causes the processor to receive () a mapping () between the set of spinal coordinate systems and a simplified coordinate system. The simplified coordinate system comprises a spinal height () descriptive of a position along the spine centerline. The simplified coordinate system further comprises a rotational orientation relative to a local vertebrae orientation. The simplified coordinate system further comprises an offset from the spine centerline. Execution of the machine executable instructions further cause the processor to repeatedly receive () a simplified coordinate () of the simplified coordinate system from the user interface. Execution of the machine executable instructions further cause the processor to repeatedly calculate () a spinal image rendering (). Calculating the spinal image rendering comprises using the mapping to transform the simplified coordinate into the set of spinal coordinate systems to determine an image location in the three dimensional medical image data. Execution of the machine executable instructions further cause the processor to repeatedly render () the spinal image rendering on the display.

Simplified navigation of spinal medical imaging data

The present invention provides an improved ultrasound imaging system arranged to evaluate a set of acquired 3D image data in order to provide a compounded 3D image of a fetus irrespective of its position and movement. This is achieved by providing an ultrasound imaging system comprising: an ultrasound probe having an ultrasound transducer array operable to acquire at different look directions a plurality of three dimensional (3D) ultrasound image frames of a volumetric region comprising a fetus; a compound image memory for storing the acquired plurality of the 3D ultrasound image frames and an articulated fetal model with a common fetal structure; an ultrasound image processor responsive to the plurality of 3D ultrasound image frames, said processor comprising a fetal segmentation unit arranged to segment each 3D image frame based on the articulated fetal model thereby providing a plurality of spatially related 3D images of the volumetric region; and an image quality analyzer coupled to the segmentation unit and arranged to determine, based on the articulated fetal model, an overall confidence value of the plurality of the 3D images, said image quality analyzer is further arranged to compare the overall confidence value with an image compounding threshold.

3D image compounding for ultrasound fetal imaging

Iterative material decomposition of multispectral image data includes providing a plurality of spectral images of a region of interest comprising a body part and a plurality of sets of material coefficients for a plurality of materials. Each spectral image is decomposed into a plurality of material images and an offset image. At least one of the material images for each spectral image is manipulated on the basis of at least one topological constraint relating to the body part to determine for each spectral image an updated plurality of material images and an updated offset image. A plurality of spectral images is then recomposed from the corresponding updated plurality of material images and the updated offset. Intensities at image locations are compared, and the updated plurality of material images is modified. The steps are repeated until convergence, and at least one of the recomposed spectral images at convergence is output.

Apparatus for iterative material decomposition of multispectral data

There is provided a computer implemented method () for medical image processing. The method comprises providing () a database of medical images and providing () an initial machine learning model which is trained for segmenting or classifying a medical feature in the medical images. The method also comprises extracting () a subset of medical images from the database based on a similarity score of the medical images and training () the machine learning model using the extracted subset of medical images in order to provide a refined machine learning model.

Computer-implemented method to increase the accuracy of machine learning models for medical image processing

An image processing system and related method. The system comprises an input interface (IN) configured for receiving an n[&#x2265;2]-dimensional input image with a set of anchor points defined in same, said set of anchor points forming an input constellation. A constellation modifier (CM) is configured to modify said input constellation into a modified constellation. A constellation evaluator (CE) configured to evaluate said input constellation based on said hyper-surface to produce a score. A comparator (COMP) is configured to compare said score against a quality criterion. Through an output interface (OUT) said constellation is output if the score meets said criterion. The constellation suitable to define a segmentation for said input image.

Robust pulmonary lobe segmentation

The present invention relates to an ultrasound diagnosis apparatus (), in particular for analyzing a fetus (). An ultrasound data interface () is configured to receive 3D (three dimensional) ultrasound data from an object (). The ultrasound diagnosis apparatus further comprises a measurement unit () for measuring anatomical structures of the object based on the segmentation data and a calculation unit () configured to calculate at least one biometric parameter based on the 3D ultrasound data.

Ultrasound diagnosis apparatus

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Uelzen, Germany

Tobias Klinder