Brainlab Ag

Andreas Blumhofer

Jens Schmaler

Stefan Vilsmeier

Kajetan Berlinger

Bálint Varkuti

Rainer Lachner

Andreas Giese

Stefan Achatz

Cornel Schlossbauer

Patrick Hiepe

Bálint Várkuti

Stephan Fröhlich

Stephan Fr&ouml;hlich

Robert Lucht

Mona Frommert

Stefan Seifert

Corinna Seltz

Yannic Meurer

Corinna Seitz

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 of segmenting a medical patient image using an atlas and relating the segmentation result to a model of possible geometric changes to the segmentation result (e.g. for correcting the position of the segmentation of anatomical structures) which consider for example anatomical limitations. The thus-related segmentation result may be used as a basis for changing and/or correcting the position, shape and/or orientation of at least parts of the segmentation result, e.g. by user interaction. The invention also relates to an atlas data set comprising information such as values of the variables of the model of possible geometric changes in relation to the positions of anatomical structures in the atlas.

Constrained object correction for a segmented image

A compartmentalized dynamic anatomic atlas is disclosed, comprising static atlas data comprising spatial element data and element representation data, wherein the spatial element data describes spatial properties of a spatial atlas element and wherein the element representation data describes representational properties assignable to the spatial atlas element, the atlas further comprising dynamic atlas data comprising information on a dynamic property which information is respectively linked to the spatial atlas element.

Compartmentalized dynamic atlas

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 dynamic anatomic atlas is disclosed, comprising static atlas data describing atlas segments and dynamic atlas data comprising information on a dynamic property which information is respectively linked to the atlas segments.

Heatmap and atlas

Disclosed is a computer-implemented method for generating an anonymized medical image of an anatomical body part of a patient, a corresponding computer program, a program storage medium storing such a program and a computer for executing the program, as well as a medical system comprising an electronic data storage device and the aforementioned computer. The disclosed method encompasses establishing a mapping from a patient image onto an atlas, changing that mapping, and applying the inverse of the changed mapping to the atlas in order to transform image content from the atlas to the patient image in order to achieve a deformed and thereby anonymised appearance of the patient image.

Anonymisation of medical patient images using an atlas

Disclosed is a computer-implemented method of determining an atlas element. The method encompasses acquiring model image data that describes at least one fixed element (an anatomical element such as an anatomical body part, for example, a rib). Patient image data is acquired that describes an improvable element (an anatomical element such as an anatomical body part, for example heart). Region data is acquired, for example by assigning a homogeneous grey value to a region of the model image. The patient image is matched to the model image, wherein no matching is performed within the region. A transformation for mapping the improvable element into the region is determined based on matching. Several patient images are then mapped into the region and superimposed. An atlas element is determined based on the superimposed images. The method may be repeated using the determined anatomical atlas element as a constraint to detect further atlas elements.

Optimizing an atlas

A data processing method for determining a path of a neural fibre in a patient, comprising the steps of:

Localization of fibrous neural structures

A matching transformation is determined for matching a patient image set of images of an anatomical body structure of a patient with an atlas image set of images of a general anatomical structure including anatomical atlas elements. Atlas spatial information containing spatial information on the general anatomical structure, and element representation information are obtained. The element representation information describes representation data sets which contain information on representations of the plurality of atlas elements in the atlas images to be determined are obtained, and also describes a determination rule for determining respective representation data sets for respective atlas elements in accordance with different respective parameter sets. Patient data is acquired by acquiring the patient image set and the parameter sets which are respectively associated with the images of the patient image set. The matching transformation is determined by matching images associated with the same parameter set to each other.

Growing community of inventors

Neubiberg, Germany

Andreas Blumhofer