Siemens Aktiengesellschaft

Siemens Healthcare Gmbh

Siemens Corporation

Technische Universitat Munchen

Thomas Boettger

Gerhard Kraemer

Grzegorz Soza

Carsten Thierfelder

Thilo Hannemann

Daniel Lerch

Philipp Hoelzer

Stefan Reichelt

Christophe Della Monta

Ali Kamen

Florian Hilbig

Alexandre Avranches

Mark Hastenteufel

Andre Wichmann

Dorin Comaniciu

Leo John Grady

Tommaso Mansi

Eike Rietzel

Alexander Gemmel

Ali Khamene

Jingdan Zhang

Jerome Declerck

Hari Sundar

Fernando Vega

Johannes Fieres

Iwan Kawrakow

Lisa Vallines

Dominique Toepfer

Francois Carnis

Bernhard Fuerst

Gerhard Krämer

Selma Zehner

A method and system are disclosed for outputting augmented reality information to a first user. In an embodiment, the method includes acquiring first information, including image information, depth information, coordinate information and combinations thereof, the first information relating to at least one of a medical device and a medical examination of a patient; creating the augmented reality information, relating to the medical device and/or the medical examination of the patient, based on the first information; and outputting the augmented reality information such that the augmented reality information is perceivable in a field of view of the first user.

Method and system for outputting augmented reality information

A method is for positioning a positionable table for a patient inside a medical imaging device. In an embodiment, the method includes a determination of a table position as a function of an organ or body part of the patient for examination; an ascertainment of correction data for correcting the table position; a determination of a corrected table position based on the ascertained correction data; and a positioning of the table at the corrected table position.

Method for positioning a positionable table

Remote control for electromedical device

Dose values are displayed. The dose values display the take-up of radiation by an examination volume to be expected during an irradiation. An examination volume is segmented in an image, and the dose values are assigned to areas of the surface of the examination volume. The surface of the examination volume is displayed as a plane such that the areas displayed as flat will be graphically encoded by the dose values assigned to the respective areas.

Display of dose values for planning an irradiation

A method for separating tissue classes in MR images is presented. The method includes acquiring a plurality of magnetic resonance images of a subject with different acquisition parameters and generating a multi-dimensional intensity distribution model from the plurality of magnetic resonance images. The multi-dimensional intensity distribution model represents a distribution of intensities of voxels in each magnetic resonance image of the plurality. The method also includes identifying clusters of correlated intensities in the multi-dimensional intensity distribution model and assigning the clusters into one or more tissue classes based on the correlated intensities of each of the cluster.

Method and system for separating tissue classes in magnetic resonance images

A method and system for prediction of respiratory motion from 3D thoracic images is disclosed. A patient-specific anatomical model of the respiratory system is generated from 3D thoracic images of a patient. The patient-specific anatomical model of the respiratory system is deformed using a biomechanical model. The biomechanical model is personalized for the patient by estimating a patient-specific thoracic pressure force field to drive the biomechanical model. Respiratory motion of the patient is predicted using the personalized biomechanical model driven by the patient-specific thoracic pressure force field.

System and method for prediction of respiratory motion from 3D thoracic images

A method for determining a 4D plan for carrying out intensity-modulated radiation therapy of a target volume subject to irregular periodic motion, with a radiation therapy apparatus is provided. The method includes selecting positions of a radiation source. The number of positions is selected to be identical in all 3D radiation therapy plans. The method also includes selecting a number of respective aperture settings assigned to a respective position of the radiation source to be identical in all 3D radiation therapy plans. A geometrical, temporal, and/or dynamic restriction that restricts a change of the aperture from one aperture setting to another aperture setting is predetermined, and the 3D radiation therapy plans are determined such that the 3D radiation therapy plans fulfill predetermined restrictions for aperture settings in each case.

Method for determining a 4D plan for carrying out intensity-modulated radiation therapy

A method for segmenting an image includes registering an annotated template image to an acquired reference image using only rigid transformations to define a transformation function relating the annotated template image to the acquired reference image. The defined transformation function is refined by registering the annotated template image to the acquired reference image using only affine transformations. The refined transformation function is further refined by registering the annotated template image to the acquired reference image using only multi-affine transformations. The twice refined transformation function is further refined by registering the annotated template image to the acquired reference image using deformation transformations.

Hierarchical atlas-based segmentation

The present embodiments relate to a method for registering medical image data. The method includes providing a registration parameter set for each of a plurality of different application cases, each of the plurality of different application cases being defined by at least one feature. Each registration parameter set contains registration parameters predefined for the associated application case. The method also includes providing first image data and second image data, which are to be registered onto one another. In response to the specification of an application case for the first image data and the second image data to be registered, a registration parameter set is automatically selected from the provided registration parameter sets in accordance with the specified application case. The method includes automatically performing a registration of the first image data onto the second image data using the selected registration parameter set.

Method and device for registering medical image data

The present embodiments relate to a method for checking a segmentation of a structure in image data, where the segmentation to be checked provides a segmentation region that approximates the structure imaged in the image data and that is delimited by a segmentation contour. The method includes spatially resolved automatic determination of characteristic values that are suitable for serving as an indicator of the accuracy of the segmentation of the structure on the basis of the image data, the segmentation contour to be checked, or a segmentation method applied during the performance of the segmentation to be checked. The method also includes spatially resolved automatic determination of confidence values for a plurality of positions on the segmentation contour using the characteristic values determined for the corresponding positions. The confidence values may indicate the reliability of the segmentation to be checked at the corresponding positions.

Method for checking the segmentation of a structure in image data

Methods and systems dedicated to automatic object segmentation from image data are provided. In a first step a fuzzy seed set is generated that is learned from training data. The fuzzy seed set is registered to image data containing an object that needs to be segmented from a background. In a second step a random walker segmentation is applied to the image data by using the fuzzy seed set as an automatic seeding for segmentation. Liver segmentation, lung segmentation and kidney segmentation examples are provided.

Methods and systems for fully automatic segmentation of medical images

Growing community of inventors

Heidelberg, Germany

Thomas Boettger