Koninklijke Philips Corporation N.v.

Axel Saalbach

Rafael Wiemker

Tim Philipp Harder

Tom Brosch

Jens Von Berg

Karsten Sommer

Juergen Weese

Thomas Buelow

Alexandra Groth

Tobias Klinder

Daniel Bystrov

Julien Senegas

Martin Bergtholdt

Kongkuo Lu

Hrishikesh Narayanrao Deshpande

Steffen Weiss

Stewart Young

Yuechen Qian

Thomas Netsch

Peter Mazurkewitz

Michael Gunter Helle

Michael Grass

Jochen Peters

Irina Waechter-Stehle

Ulrich Wolfgang Katscher

Eric Cohen-Solal

Christopher Stephen Hall

Jochen Keupp

Fabian Wenzel

Kirsten Regina Meetz

Ingmar Graesslin

Sabine Mollus

Rolf Jürgen Weese

Gabriel Ryan Mankovich

Dirk Schäfer

Wilhelmus Henrica Gerarda Maria Van Den Boomen

Michael Chun-Chieh Lee

Ranjith Naveen Tellis

Alexander Schmidt-Richberg

Tanja Nordhoff

Johannes Buurman

Ran Cohen

André Goossen, Sr

Pieter Gerben Eshuis

Irina Wächter-Stehle

Christophe Michael Jean Schulke

Bela Fadida

Lior Wolloch

Christophe Schuelke

Ivo Matteo Baltruschat

André GOOßEN

Christopher Brian Martel

A computer implemented method of making a measurement associated with a feature of interest in an image. The method comprises using () a model trained using a machine learning process to take the image as input and predict a pair of points between which to make the measurement of the feature of interest in the image. The method then comprises determining () the measurement, based on the predicted pair of points.

Making measurements in images

When performing an imaging scan on a patient, x-ray tube currents are modulated as the scan is performed. The x-ray tube current values and modulations may be recorded and accessed. The accessed values may be processed to generate overlays and displays for identifying patient and diagnostic device issues.

Systems and methods for processing and visualizing tube current modulations in medical imaging devices

In a conventional system for preparing reports on findings in medical images, the actual formulation of the report is not or only insufficiently supported by the computer-based system. Although there are efforts to improve such a system through automatic reporting, however, in previous systems, the error rate is too high and/or the operation of the system too complicated. This application proposes to provide text prediction to a user on the display device. The text prediction is based on prior analyzing the image content of a medical image at is displayed to the user at least when the user activates a text field shown on the display device via the input unit. The displayed text prediction is selected from a pre-defined set of text modules that are associated to the analysis result.

Automatic diagnosis report preparation

A system for reviewing a prior medical image and a related prior medical report, including: a plurality of measurement tools configured to: receive an input medical image and information regarding a medical finding; analyze the input medical image to determine a measurement relating to the medical finding; and output the measurement relating to the medical finding; a report analyzer configured to: receive the prior medical report; analyze the prior medical report to extract first information regarding a first medical finding described in the medical report; select a first measurement tool of the plurality of measurement tools to analyze the prior medical image based upon the first extracted information; and output the first extracted information to the first measurement tool, wherein the first measurement tool analyzes the prior medical image to produce a first updated measurement of the first medical finding and the first measurement tool analyzes a new medical image associated with the prior medical image to produce a new first measurement of the first medical finding; a report generator configured to receive the new first measurement and to produce a new report based upon the new first measurement.

Guided reviews of prior exams and reports

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

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

A magnetic resonance imaging system including a memory configured to store machine executable instructions, pulse sequence commands, and a first machine learning model including a first deep learning network. The pulse sequence commands are configured for controlling the magnetic resonance imaging system to acquire a set of magnetic resonance imaging data. The first machine learning model includes a first input and a first output, a processor, wherein execution of the machine executable instructions causes the processor to control the magnetic resonance imaging system to repeatedly perform an acquisition and analysis process including: acquiring a dataset including a subset of the set of magnetic resonance imaging data from an imaging zone of the magnetic resonance imaging system according to the pulse sequence commands, providing the dataset to the first input of the first machine learning model, in response to the providing, receiving a prediction of a motion artifact level of the acquired magnetic resonance imaging data from the first output of the first machine learning model, the motion artifact level characterizing a number and/or extent of motion artifacts present in the acquired magnetic resonance imaging data.

Motion artifact prediction during data acquisition

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

The present disclosure relates to a computer implemented medical analysis method for predicting metastases () in a test tissue sample, the method comprising: providing a first machine learning model () having an input and an output, receiving a description () of a tumor () and first image data () of a test tissue sample of an anatomy region (), the test tissue sample being free of metastases (), providing the first image data () and the tumor description () to the input of the first machine learning model (), in response to the providing, receiving from the output of the first machine learning model () a prediction of occurrence of metastases () originating from the tumor () in the test tissue sample, and providing the prediction.

Medical analysis method for predicting metastases in a test tissue sample

There is provided a computer-implemented method and system () for determining regions of hyperdense lung parenchyma in an image of a lung. The system () comprises a memory () comprising instruction data representing a set of instructions and a processor () configured to communicate with the memory and to execute the set of instructions. The set of instructions, when executed by the processor (), cause the processor () to locate a vessel in the image, determine a density of lung parenchyma in a region of the image that neighbours the located vessel, and determine whether the region of the image comprises hyperdense lung parenchyma based on the determined density, hyperdense lung parenchyma having a density greater than &#x2212;800 HU.

Determining regions of hyperdense lung tissue in an image of a lung

The invention provides for a magnetic resonance imaging system (). The execution of machine executable instructions causes a processor () controlling the magnetic resonance imaging system to control () the magnetic resonance imaging system to acquire the magnetic resonance imaging data () using pulse sequence commands () and reconstruct () a magnetic resonance image (). Execution of the machine executable instructions causes the processor to receive () a list of suggested pulse sequence command changes () by inputting the magnetic resonance image and image metadata () into an MRI artifact detection module (&#x2033;). The MRI artifact detection module comprises at least one neural network, which has been trained using images from failed magnetic resonance imaging protocols and/or magnetic resonance data extracted from the magnetic resonance imaging protocols labeled as failed accessed from a log file () which logs the execution of previous magnetic resonance imaging protocols. Execution of the machine executable instructions further causes the processor to receive () a selection of a chosen pulse sequence command change () from the list of suggested pulse sequence command changes. Execution of the machine executable instructions further causes the processor to modify () the pulse sequence commands using the chosen pulse sequence command change.

Automatic artifact detection and pulse sequence modification in magnetic resonance imaging

The invention relates to a magnetic resonance imaging data processing system () for processing motion artifacts in magnetic resonance imaging data sets using a deep learning network () trained for the processing of motion artifacts in magnetic resonance imaging data sets. The magnetic resonance imaging data processing system () comprises a memory () storing machine executable instructions () and the trained deep learning network (). Furthermore, the magnetic resonance imaging data processing system () comprises a processor () for controlling the magnetic resonance imaging data processing system. Execution of the machine executable instructions () causes the processor () to control the magnetic resonance imaging data processing system () to: receive a magnetic resonance imaging data set (), apply the received magnetic resonance imaging data set () as an input to the trained deep learning network (), process one or more motion artifacts present in the received magnetic resonance imaging data set () using the trained deep learning network ().

Deep learning based processing of motion artifacts in magnetic resonance imaging data

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

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

The invention relates to an apparatus for identifying a candidate object in image data and determining a likelihood that the candidate object is an object from an object class. The apparatus comprises an image data receiving unit for receiving image data of an object of the object class, a seed element selecting unit for selecting a portion of the image elements as seed elements, a contour point identifying unit for identifying, for each seed element (SE), contour points, the contour points of a seed element circumscribing a candidate object which comprises the seed element, and a seed score determining unit for determining, for each seed element, a seed score indicative of a likelihood that the candidate object is an object from the object class. The invention allows differentiation between an object of an object class of interest and artifacts.

Apparatus for identifying objects from an object class

Growing community of inventors

Hamburg, Germany

Axel Saalbach