Koninklijke Philips Corporation N.v.

Koninklijke Philips Electronics N.v.

Philips Gmbh

Philips Intellectual Property & Standards Gmbh

Jens Von Berg

Stewart Young

Cristian Lorenz

Daniel Bystrov

Tobias Klinder

Axel Saalbach

Raoul Florent

Rafael Wiemker

Gereon Vogtmeier

Julien Senegas

Steffen Renisch

Thomas Blaffert

Juergen Weese

Jochen Peters

Eric Cohen-Solal

Michael Kaus

Sven Kabus

Tim Philipp Harder

Ravindra Bhat

Tom Brosch

Thomas Koehler

Michael Grass

Roland Proksa

Roger Steadman Booker

Tim Nielsen

Hanns-Ingo Maack

Michael Harald Kuhn

Irina Waechter-Stehle

Heinrich Schulz

Alexandra Groth

Christopher Stephen Hall

Vladimir Pekar

Fabian Wenzel

Kirsten Regina Meetz

Sebastian Peter Michael Dries

Claire Levrier

Nataly Wieberneit

Hans Barschdorf

Sven Kroenke

Stefanie Remmele

Torbjorn Vik

Ulrich Neitzel

Olivier Ecabert

Biswaroop Chakrabarti

Soubhik Paul

Sven Krönke

Prasad Adhikarath Valappil

Liesbet Hilde Hadewijch Roose

Christian Lorenz

Nicole Schadewaldt

Jörg Sabczynski

Joerg Sabczynski

Sherif Makram-Ebeid

Pallavi Vajinepalli

Shlomo Gotman

Olivier Gerard

Jean-Michel Rouet

Astrid Ruth Franz

Michael Chun-Chieh Lee

Ingwer Curt Carlsen

Maxim Fradkin

Alexander Schmidt-Richberg

Heike Carolus

Andre Goossen

Bernd Hofmann

Roland Johannes Opfer

Michael Chun-chieh Lee

Dirk Manke

Christoph Kurze

Sudipta Chaudhury

André Goossen, Sr

Anand Kumar Dokania

Bernd Lundt

André Goossen

Luna Fahoum

Stefan Matthias Florian Schmidt

Sven Kroenke-hille

André Gooβen

Heiner Matthias Brueck

Ivo Matteo Baltruschat

Marc Hensel

Hauke Schramm

There is provided a computer-implemented method for determining rotation of a patient's chest in a medical image. The method comprises: receiving the medical image of the patient; processing the medical image to determine scapular spatial data relating to the patient's scapula; and determining rotation of the patient's chest with respect to at least one reference axis using the scapular spatial data.

Determining rotation of a patient's chest in a medical image

A computer-implemented method for positioning a subject in medical imaging, comprising: receiving a first image () of a region of interest () of the subject (S); determining first positioning data based on the first image (), wherein the first positioning data indicates an alignment of the region of interest () relative to a first image acquisition unit used to acquire the first image () (S); determining guidance data based on the first positioning data, wherein the guidance data comprises a guidance for an alignment of the region of interest () relative to a second image acquisition unit used to acquire a second image () from a current alignment to a target alignment, wherein the target alignment is to correspond to that derived from the first positioning data (S); providing the guidance data for acquiring the second image () (S).

Guidance for positioning a patient in medical imaging

An orientation of a patient's chest is determined by an apparatus and method. According to the invention, a camera image of a patient comprising image data of the patient's chest is received, wherein the camera image is acquired with an optical camera. Further, an X-ray radiograph of the patient's chest with an X-ray imaging axis extending from an X-ray source to an X-ray detector is received. Then, the camera image and the X-ray radiograph are provided to at least one processor. Further, information relating to a clavicle to spinous process distance of the patient is then determined using the camera image. Finally, an orientation of the patient's chest in the X-ray radiograph with respect to the X-ray imaging axis is determined using the camera image, the X-ray radiograph, and the information relating to the clavicle to spinal process distance of the patient.

Apparatus for determining an orientation of a patient's chest

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

The present invention relates to landmark and/or temporal event detection. It is proposed to utilize previously learned spatial statistical correlations between multiple landmarks in order to regularize convolutional neural networks (CNNs) either as a post-processing step or during training in order to utilize anatomical prior knowledge, reduce the false-positive prediction rate, and/or increase the accuracy and stability of the algorithm. The proposed apparatus and method may also be applied to improve the detection of correlated events in e.g., time-series by leveraging prior knowledge.

Probabilistic regularization of convolutional neural networks for multiple-feature detection based on correlations

In order to enhance enhanced X-ray image inhalation quality monitoring, a metric is proposed hat reproducibly provides an index of ribs visible to be used in the assessment of the inhalation state. In an example, a detected diaphragm in a chest X-ray image may be projected into an atlas that contains labels for all intercostal spaces, namely spaces between rib centerlines. A spatial representation of both the clavicle and the ribs is provided in the atlas, a cumulative histogram is built for all points, i.e. pixels, of the diaphragm, for every point a rib label counter of the rib in the rib label map at that point is incremented as well as all ribs above it, the rib label counter is normalized by a division by the number of points, the median (or a different quantile) may be taken of this distribution serving as an inhalation index. An objective metric of inhalation state is thus achieved.

Inhalation metric for chest x-ray images

The invention relates to a method () for supervised training of an artificial neural network for medical image analysis. The method comprises acquiring (SI) first and second sets of training samples, wherein the training samples comprise feature vectors and associated predetermined labels, the feature vectors being indicative of medical images and the labels pertaining to anatomy detection, to semantic segmentation of medical images, to classification of medical images, to computer-aided diagnosis, to detection and/or localization of biomarkers or to quality assessment of medical images. The accuracy of predetermined labels may be better for the second set of training samples than for the first set of training samples. The neural network is trained (S) by reducing a cost function, which comprises a first and a second part. The first part of the cost function depends on the first set of training samples, and the second part of the cost function depends on a first subset of training samples, the first subset being a subset of the second set of training samples. In addition, the second part of the cost function depends on an upper bound for the average prediction performance of the neural network for the first subset of training samples and the second part of the cost function is configured for preventing that the average prediction performance for the first subset of training samples exceeds the upper bound.

Constrained training of artificial neural networks using labelled medical data of mixed quality

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

There is provided a computer-implemented method of estimating lung volume from radiographic images. The method comprises: registering () a two dimensional radiographic image () of a patients chest to a three dimensional radiographic image () of the patients chest to estimate data describing projection geometry () of an imaging setup used to capture the two dimensional radiographic image; using the projection geometry to estimate () at least one radiographic magnification factor () relating to the imaging setup; and calculating () an estimated lung volume () using the two dimensional radiographic image and the at least one radiographic magnification factor.

Lung volume estimation from radiographic images

The field of view of an X-ray imaging system should be set appropriately to ensure that anatomical information of interest is not omitted. In particular, it is necessary to ensure that the operator of an X-ray system does not allow a patient to leave the X-ray imaging system until it is certain that the correct anatomy has been imaged. This application discusses a technique enable the visualization of a field of view boundary error caused by the incorrect configuration of an X-ray imaging system. Optionally, the boundary error is displayed either on a user display of a system console, or by projecting the field of view error onto the patient in the X-ray system. Thus, an operator of the system may be alerted to the presence of a boundary error, enabling a new X-ray exposure to be taken, if necessary.

Visualizing collimation errors

Medical radiography requires specialist control of radiography equipment to achieve good imaging results. Typical errors that can occur consist of an inappropriate field of view being accidentally applied. This results in a 'cropping effect' in which portions of the region of interest of a patient which would be of clinical use are omitted from the image. Conventionally, the only solution is to re-take the entire image with a more appropriate (and inevitably larger) field of view selected. This is undesirable, because it might require recall of the patient from another location, and the patient will be subject to two exposures, thus undesirably increasing their X-ray dosage. The present application proposes to use an anatomical atlas to analyse an X-ray image output from an initial exposure, in particular to assess whether significant anatomical elements are missing from the image. If elements are missing, the field of view of the X-ray imager is recalibrated, using collimation or pan/tilt adjustments, for example. A subsequent X-ray image is obtained, and combined with the initial image, to provide an output image. Because only a small area of the region of interest may subsequently need to be exposed, a smaller additional dose results.

X-ray apparatus having a composite field of view

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

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 apparatus for the detection of opacities in X-ray images. It is described to provide () an analysis X-ray image of a region of interest of an analyzed body part. A model of a normal region of interest is provided (), wherein the model is based on a plurality of X-ray images of the region of interest. At least one abnormality is detected () in the region of interest of the analyzed body part, the detection comprising comparing the analysis X-ray image of the region of interest and the model of the normal region of interest. Information is output () on the at least one abnormality.

Apparatus for the detection of opacities in X-ray images

The present invention relates to a device () for determining positioning data for an X-ray image acquisition device () on a mobile patient support unit (), the device comprising: a processing unit (); and a status detector (); wherein the status detector () is configured to acquire geometry data and type data of a mobile patient support unit () and of an X-ray image acquisition device () on the mobile patient support unit (), and to transmit a status signal comprising the geometry data and the type data; wherein the processing unit () is configured to receive the status signal and data about region of interest position on the patient; and wherein, based on the status signal and the region of interest position, the processing unit () is configured to determine positioning and alignment data for an X-ray image acquisition device () on the mobile patient support unit (), and to provide an image acquisition protocol for the X-ray image acquisition device (). The device () may provide an X-ray image acquisition system having a simple and easy to operate construction.

Device and method for determining positioning data for an X-ray image acquisition device on a mobile patient support unit

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Hamburg, Germany

Jens Von Berg