Siemens Aktiengesellschaft

Siemens Healthcare Gmbh

Siemens Aktiengesell Schaft

Siemens Healthineers Ag

Herlev and Gentofte Hospital

Michael Sühling

Thomas Flohr

Shaohua Kevin Zhou

Tommaso Mansi

Bernhard Schmidt

Michael Scheuering

Alexey Tsymbal

David D Liu

Rui Liao

Shun P Miao

Grzegorz Soza

Anna Jerebko

Christian Hofmann

Michael Kelm

Michael Wels

Andreas Wimmer

Fernando Vega-Higuera

Thilo Hannemann

Martin Sedlmair

Stefan Reichelt

Lutz Gündel

Christian Tietjen

Thomas Pheiffer

Florin Cristian Ghesu

Jan Kretschmer

Martin Kramer

Alexander Muehlberg

Alexander Katzmann

Oliver Taubmann

Stefan Grosskopf

Felix Lades

Max Schöbinger

Matthias Thorn

Ferdinand Distler

Christian Petry

Carlos Leber

Helmut Ringl

Pavlo Dyban

Dimitrij Zharkov

Hannes Martinke

Andreas Dejon

Friedrich Knoth

Stefanie Steding

Rainer Kärgel

Stephan Ahmad

Alexander Mühlberg

Mathias Willadsen BREJNEBØL

Christoph Felix MÜLLER

A method is disclosed for the automatic determination of the bone density and a method is disclosed for the automatic detection and characterization of spinal column fractures. Both methods enable the fully automatic detection and assessment of damaged vertebrae and reliably enable an analysis of the state of the vertebrae with a high accuracy rate. A computed tomography system to carry out either of the methods is further disclosed.

Method for the fully automatic detection and assessment of damaged vertebrae

For therapy response assessment, texture features are input for machine learning a classifier and for using a machine learnt classifier. Rather than or in addition to using formula-based texture features, data driven texture features are derived from training images. Such data driven texture features are independent analysis features, such as features from independent subspace analysis. The texture features may be used to predict the outcome of therapy based on a few number of or even one scan of the patient.

Early therapy response assessment of lesions

A method for generating a volume-viewing image on the basis of three-dimensional image data of the interior of a body generated by way of a medical imaging system is described. In at least one embodiment, a plurality of different functional structures of the body are first of all segmented in the three-dimensional image data. Moreover, structure selection data for fixing functional structures to be visualized is determined. Then, at least one volume-viewing image is generated from the three-dimensional image data on the basis of the segmented functional structures and the structure selection data and the image is output. Moreover, in at least one embodiment, an image-processing system to carry out at least one embodiment of the method is described.

Method and image-processing system for generating a volume-viewing image of the interior of a body

An embodiment of the method is disclosed for non-invasive lesion candidate detection in a patient's body includes generating a number of first medical images of the patient's body. The method further includes identifying lesion-like geometrical regions inside the first medical images of the patient's body by applying image processing methods, whereby the identification is at least partly controlled by a number of patient-specific context features which are not directly extractable from the first medical images. In addition, the method includes selecting a number of the identified lesion-like geometrical regions as lesion candidates.

Method and system for lesion candidate detection

A method and an apparatus are disclosed for registering at least three different image data records for an object. In at least one embodiment, the method involves first of all determining for each image data record the anatomical region which is covered by the image data record. Next, degrees of overlap in these anatomical regions are calculated. These degrees of overlap are taken as a basis for determining the image data record as a master image data record which, in total, has a maximum overlap with the other image data records, ignoring degrees of overlap which are below a prescribable lower threshold. The other image data records are then registered merely with the master image data record. Thus, they are automatically also indirectly registered with one another. At least one embodiment of the present method and the associated apparatus reduce the waiting time for the user who wishes to visualize a plurality of the image data records simultaneously next to one another or superimposed on one another.

Method and apparatus for registering at least three different image data records for an object

A visualization of an image data record of an organ enclosing a cavity, in particular a CT image data record of a colon, that is reliable and has a low level of computational complexity, is performed according to a method. In at least one embodiment of the method a virtual viewer position outside the organ tissue is defined; an interface between the organ tissue and the cavity is defined with the aid of the image data record; from the middle of the cavity local gradients, that specify the rise in the absorption behavior between a gas contained in the cavity and the organ tissue, are determined; starting from the viewer position, a search beam is defined and an angle between the search beam and the gradients is determined; and a transparency value is allocated to the organ tissue as a function of the angle during visualization.

Method and apparatus for visualizing an image data record of an organ enclosing a cavity, in particular a CT image data record of a colon

A method is described for evaluating a tomography data record. In at least one embodiment, a tomography data record of a hollow organ is generated, the tomography data record is analyzed automatically, and organ sections to be assessed as critical are determined. Further, a corresponding findings list entry is generated in a findings list as a reaction to a determined organ section to be assessed as critical, various organ sections are displayed successively on the basis of the tomography data record, and after its first display, an organ section assessed as critical can be selected for renewed display directly via the findings list. Furthermore, a corresponding tomography workstation is described.

Method for evaluating a tomography data record, and a tomography workstation

A method is disclosed for the segmented representation of vessel-like structures of an object under examination, on the basis of tomographic data, wherein a three-dimensional tomographic volume data record of the object under examination is generated and a segmentation is carried out which enhances the vessel-like structures in the representation of the tomographic data. According to an embodiment of the invention, for each voxel, the probability with which the voxel is located in a vessel structure is determined from the environmental data of the voxel with the aid of a vessel-specific filter of a spatial dimension which corresponds to the tomographic volume data record, on the basis of Gaussian functions, and these determined probabilities are additionally used as criterion for the presence of a vessel in the segmentation process for the representation of vessel structures. An embodiment of the invention also relates to a tomography system, with a device for scanning an object under examination, preferably a patient, and at least one computer system for editing tomographic image data records containing a memory for storing program code and a processor system for executing the programs, wherein program code is stored which executes the method steps of an embodiment of the method.

Method for vessel enhancement and segmentation in 3-D volume data

A method is disclosed for identification of a contrasted blood vessel in digital 3D image data, the method using a generic region-growing algorithm with several steps, for which among other things seed points are searched for and, in dependence on a current threshold value, are assigned to corresponding seed point sets. The number of seed points which are associated during a specific section of the method to the same seed point set with the threshold value is recorded and a leakage signal is produced if the number exceeds a maximum value. When the leakage signal occurs, those seed points which have been associated with the threshold value during that section are not stored as vessel voxels, the method is terminated at the location of the leakage and the remaining method is continued.

Method for identification of a contrasted blood vessel in digital image data

Growing community of inventors

Erlangen, Germany

Michael Sühling