Brainlab Ag

Brainlab Med. Computersysteme Gmbh

Brainlag Ag

Brainlab Med. Computersysteme Gmbb

Medphoton Gmbh

Stefan Vilsmeier

Rainer Birkenbach

Jens Schmaler

Michael Bertram

Andreas Blumhofer

Rainer Lachner

Timo Neubauer

Stephan Erbel

Christian Brack

Thomas Feilkas

Ingmar Hook

Stefan Lippstreu

Hagen Kaiser

Christoffer Hamilton

Claus Schaffrath

Uli Mezger

Mario Zeiss

Falko Seifferth

Thomas Bauch

Patrick Hiepe

Stephen Froehlich

Clara Orlando

Ron Zenvirt

Kajetan Berlinger

Mario Schubert

Gregor Tuma

Johannes Manus

Bálint Varkuti

Wolfgang Steinle

Robert Schmidt

Jens Witte

Stephan Froehlich

Cornel Schlossbauer

Jürgen Gassner

Valentin Elefteriu

Stephan Fr&ouml;hlich

Akos Dombay

Joseph Doyle

Stefan Seifert

Heinrich Deutschmann

Eric Merlin

Daniel Schaffarzick

Ulf Labsik

Ueli Graf

Matthias Eimer

Juergen Gassner

Alexander Pfäffle

Ivan Messner

Phillip Steiniger

Daniel Kellner

Michael Hubauer

Mathias Kraihamer

Joachim Spielbichler

Andreas Blumenhofer

Disclosed are computer-implemented methods which encompass determining whether two medical images were taken of the same patient. In a first aspect, this is done by analysing a registration of the two images with one another. The registration may be a direct registration between the two images or an indirect registration, for example via an atlas to which each image is registered. In other aspects, a machine learning algorithm is trained on the basis of image registrations to determine whether the two images were taken of the same patient. The disclosed methods serve the purpose of being able to group medical images together which were taken of the same patient without having to provide or otherwise process data about the identity of the patient.

Determining image similarity by analysing registrations

Disclosed is a computer-implemented method of transmitting identification information of a medical instrument. The method encompasses comparing a digital image of an instrument tray and an instrument to a digital image of just the instrument tray to determine the identity of the instrument. A characteristic geometry such as its envelope is assigned to the instrument, and a characteristic quantity of the envelope such as its aspect ratio may be used to identify the instrument. Based on determining, from the image of the instrument and the instrument tray, the relative position between those two entities, the method determines whether the instrument has been taken from the instrument tray, and accordingly instructs a medical computing system about this determination. The medical computing system may then determine whether the correct instrument has been taken from the instrument has been taken from the instrument tray, for example by comparison with medical procedure planning data.

Image-based instrument identification and tracking

Disclosed is a computer-implemented of adapting a biomechanical model of an anatomical body part of a patient to a current status of the patient. The method encompasses determination of a currently executed step of a workflow such as a medical intervention, the result of the determination serving as a basis for adapting and/or updating a biomechanical model of an anatomical body part to the corresponding current status of the patient. The determination of the current workflow step may also be used as basis for controlling an imaging device for tracking entities around the patient or for imaging the anatomical body part or acquiring further data or for urging the user to perform a specific action such as acquisition of information using a tracked instrument such as a pointer. The biomechanical model has been generated from atlas data. The data sets which are generated according to the current workflow step may additionally or alternatively serve as a basis for determining the current workflow step and/or adapting the further workflow.

Adapting a biomechanical model for a patient

Disclosed is a computer-implemented method which encompasses registering a tracked imaging device such as a microscope having a known viewing direction and an atlas to a patient space so that a transformation can be established between the atlas space and the reference system for defining positions in images of an anatomical structure of the patient. Labels are associated with certain constituents of the images and are input into a learning algorithm such as a machine learning algorithm, for example a convolutional neural network, together with the medical images and an anatomical vector and for example also the atlas to train the learning algorithm for automatic segmentation of patient images generated with the tracked imaging device. The trained learning algorithm then allows for efficient segmentation and/or labelling of patient images without having to register the patient images to the atlas each time, thereby saving on computational effort.

Medical image analysis using machine learning and an anatomical vector

Disclosed is a medical data processing method for determining an indicator relating to an injury of an anatomical structure () of a patient, wherein the method comprises executing, on at least one processor () of at least one computer (), steps of:

Determining an indicator relating to injury

The present invention relates to a computer-implemented medical method, a computer program and a medical system () for providing, in a medical environment, an AR-overlay that is projected into the field of view provided by an AR-device () as a function of the characteristics of a user's () view with respect to an observed object ().

Visualization of medical data depending on viewing-characteristics

A medical image processing method performed by a computer, for measuring the spatial location of a point on the surface of a patient's body including: acquiring at least two two-dimensional image datasets, wherein each two-dimensional image dataset represents a two-dimensional image of at least a part of the surface which comprises the point, and wherein the two-dimensional images are taken from different and known viewing directions; determining the pixels in the two-dimensional image datasets which show the point on the surface of the body; and calculating the spatial location of the point from the locations of the determined pixels in the two-dimensional image datasets and the viewing directions of the two-dimensional images; wherein the two-dimensional images are thermographic images.

Human body measurement using thermographic images

Using a current workflow step for control of medical data processing

Disclosed is a computer-implemented methods of determining distributions of corrections for correcting the segmentation of medical image data, determining corrections for correcting the segmentation of medical image data, training a learning algorithm for determining a segmentation of a digital medical image, and determining a relation between an image representation of the anatomical body part in an individual medical image and a label to be associated with the image representation of the anatomical body part in the individual medical image using the trained machine learning algorithm. The methods encompass reading a plurality of corrections to image segmentations, wherein the corrections themselves may have been manually generated, transforming these corrections into a reference system which is not patient-specific such as an atlas reference system, conducting a statistical analysis of the correction, and applying the re-transformed result of the statistical analysis to patient images. The result of the statistical analysis may also be used to appropriately train a machine learning algorithm for automatic segmentation of patient images. The application of such a trained machine learning algorithm is also part of this disclosure.

Correcting segmentation of medical images using a statistical analysis of historic corrections

A radiotherapy feedback device is provided which provides one of a plurality of indication signals for presentation to a surgeon based on the status of a current surgical procedure. In some aspects, an indication signal is provided to the surgeon if the surgical procedure on an anatomical structure is of sufficient status so as to respond well to subsequent radiotherapy.

Radiotherapy feedback device

Disclosed is a computer-implemented method of determining an assignment of an object acquire patient image data of interest recognizable in a digital medical patient image such as a tumour or other medical anomaly such as an implant to an anatomical region. The medical patient image is registered with atlas data, The assignment is then determined by calculating a score value defining an amount of volume intersection between the object of interest and a digital object defining a specific anatomic region, for example a bounding box around a specific organ, which is defined in the atlas data.

Atlas-based location determination of an anatomical region of interest

Disclosed is a medical data processing method for determining an indicator relating to an injury of an anatomical structure () of a patient, wherein the method comprises executing, on at least one processor () of at least one computer (), steps of: a) acquiring (S) acceleration data describing an energy of a set of one or more signals in dependence on both time and frequency, the set of signals acquired by measuring the acceleration of the anatomical structure () over time; b) acquiring (S) analysis data describing an analysis rule for determining at least one of b1) an overall energy level of at least one signal of the set of signals, b2) a correlation between at least two signals of the set of signals in the frequency domain, the at least two signals respectively measured at at least two different respective regions of the anatomical structure (), or b3) a relationship between energies given for at least two different frequency ranges of at least one signal of the set of signals; c) determining (S) indicator data describing the indicator based on the acceleration data and the analysis data.

Disclosed is a computer-implemented method of determining a correspondence between a region of interest as it appears in a first digital medical patient image and as it appears in a second digital medical image. The correspondence is determined by calculating the ratio of overlap of the region of interest with a data object defining an anatomical body part in the first image and the second image and determining whether the larger of the two ratios exceeds a threshold. If the threshold is exceeded, the method assumes that the appearances in the two images describe the same region of interest.

Comparison of a region of interest along a time series of images

The present application relates to a data processing method for determining the position of a soft tissue body part within a patient's body. The data processing method includes acquiring CT-image data including information about the position of the body part within a coordinate system assigned to a transportable CT-device, wherein the patient's body is positioned relative to the treatment device, and wherein the CT-device is configured to be positioned relative to the patient's body and/or relative to the treatment device, acquiring first transformation data including information about a first transformation between the coordinate system assigned to the CT-device and a coordinate system assigned to the treatment device, and determining, based on the CT-image data and the first transformation data, position data including information about the position of the body part within the coordinate system assigned to the treatment device.

Utilization of a transportable CT-scanner for radiotherapy procedures

Growing community of inventors

Munich, Germany

Stefan Vilsmeier