Siemens Aktiengesellschaft

Siemens Healthcare Gmbh

Siemens Healthineers Ag

Siemens Corporation

Other

Michael Wels

Michael Suehling

Michael Kelm

Dorin Comaniciu

Max Schoebinger

Terrence Chen

Martin Huber

Felix Lades

Vivek Kumar Singh

Yao-Jen Chang

Grzegorz Soza

Kai Ma

Chris Schwemmer

Alexander Muehlberg

Felix Denzinger

Christian Hopfgartner

Shaohua Kevin Zhou

Mehmet Akif Gulsun

Anna Jerebko

Andreas Wimmer

Maria Jimena Costa

Serkan Cimen

Gustavo Henrique Monteiro De Barros Carneiro

Alexander Cavallaro

Suha Ayman

Ashish Jaiswal

Rumman Khan

Yefeng Zheng

Tong Fang

David D Liu

Gregory G Slabaugh

Gozde Unal

Thomas J O'Donnell

Thilo Hannemann

Peng Wang

Florin Cristian Ghesu

Sascha Seifert

Christopher Vincent Alvino

Dijia Wu

Michael Sühling

Simone Prummer

Helmut König

Alexander Katzmann

Alexey Tsymbal

Oliver Taubmann

Rainer Kaergel

Jing Lu

Max Schöbinger

Felix Durlak

Rüdiger Bertsch

Roland Brill

Ferdinand Distler

Sebastian Faby

Rainer Kärgel

Tobias Betz

Christian Schmidt

Alexander Mühlberg

At least one example embodiment provides a computer-implemented method for evaluating at least one image data set of an imaging region of a patient, wherein at least one evaluation information describing at least one medical condition in an anatomical structure of the imaging region is determined.

Computer-implemented methods and evaluation systems for evaluating at least one image data set of an imaging region of a patient, computer programs and electronically readable storage mediums

A computer-implemented method for evaluating a three-dimensional angiography dataset of a blood vessel tree of a patient, comprises determining a variant information describing a belonging to at least one anatomical variant class of a plurality of anatomical variant classes relating to anatomical variants of the blood vessel tree based on a comparison of angiography information of the angiography dataset to reference information describing at least one of the anatomical variant classes.

Computer-implemented method for evaluating a three-dimensional angiography dataset, evaluation system, computer program and electronically readable storage medium

A method, preferably a computer implemented method, and system are for providing output data associated with a medical imaging dataset of a patient, the medical imaging dataset including image data of a region of an anatomy of a patient including a plurality of coronary arteries. The output data is a significance score associated with a medical imaging data set of a patient. The method, in the most general terms, includes receiving input data, e.g. via a first interface, generating output data by applying algorithmic operations to the input data, and providing the output data, e.g. via a second interface.

Method and system for determining a significance score associated with a medical imaging dataset of a patient

A computer-implemented method is for providing radiomics-related information. In an embodiment, the computer-implemented method includes receiving radiomics-related data; determining, based on the radiomics-related data and an assistance algorithm, a function for processing the radiomics-related data; calculating, based on the radiomics-related data and the function for processing the radiomics-related data, the radiomics-related information; and providing the radiomics-related information.

Method and data processing system for providing explanatory radiomics-related information

A computer-implemented method is for evaluating a CT data set of a coronary region of a patient regarding perivascular tissue of at least one target blood vessel. In an embodiment, the computer-implemented method automatically determines a centerline of at least the at least one target blood vessel in the CT data set; defines a region of interest as including at least one defined outer region radius around an extension interval of the centerline, the outer region radius being relatively larger than a radius of the at least one target blood vessel; and calculates at least one quantitative value from CT data in the region of interest.

Computer-implemented method for evaluating a CT data set regarding perivascular tissue, evaluation device, computer program and electronically readable storage medium

A method is for providing an aggregate algorithm for processing medical data. In an embodiment, a multitude of local algorithms are trained by machine learning. The training of each respective local algorithm is performed on a respective local system using respective local training data. A respective algorithm dataset concerning the respective local algorithm is transferred to an aggregating system that generates the aggregate algorithm based on the algorithm datasets.

Method for providing an aggregate algorithm for processing medical data and method for processing medical data

A method is for determining at least one object feature of an object at least partially depicted by an object image. A respective preliminary feature for each object feature and at least one acquisition feature are determined from the object image. The preliminary feature depends on the object feature and on an imaging device used to acquire the object image and/or at least one imaging parameter used for acquiring and/or reconstructing the object image and/or an additional feature of the object. The acquisition feature depends on the imaging device and/or the imaging parameter and/or the additional feature of the object. A correction algorithm determines the object feature from the preliminary feature and the acquisition feature. The acquisition feature is determined by a respective determination algorithm that is selected and/or parametrized from a group of candidate algorithms, depending on multiple reference images.

Method for determining at least one object feature of an object

A method for interactively generating a geometric model of a volume object on the basis of three-dimensional image data of an examination region of interest of an examination subject is described. According to an embodiment, a representation of the volume object is determined on the basis of three-dimensional image data and a two-dimensional representation is determined on the basis of the determined representation using a preferably non-linear planar reformation of the three-dimensional object. Subsequently, boundary indicators which define the surface profile of the volume object are edited in the two-dimensional representation. Following the editing, a three-dimensional representation of the edited boundary indicators is generated by back-transforming the edited boundary indicators into three-dimensional space. Finally, a model-based representation of the volume object is generated in three-dimensional space on the basis of the edited boundary indicators. A volume object modeling device is also described. In addition, a medical imaging device is described.

Model-based generation and representation of three-dimensional objects

A method and a device are disclosed for the perspective representation, via an output image, of at least one virtual scene component arranged within a real scene. In the method, depth image data of the real scene is captured from a first perspective via a depth image sensor, and 2D image data of the real scene is captured from a second perspective via a 2D camera. Further, a virtual three-dimensional scene model of the real scene is created with reference to depth information from the depth image data, and at least one virtual scene component is inserted into the three-dimensional virtual model. Finally, an output image is generated by way of perspective projection of the 2D image data corresponding to the second perspective onto the virtual three-dimensional scene model comprising the virtual scene component.

Perspective representation of a virtual scene component

A method for estimating a body surface model of a patient includes: (a) segmenting, by a computer processor, three-dimensional sensor image data to isolate patient data from environmental data; (b) categorizing, by the computer processor, a body pose of the patient from the patient data using a first trained classifier; (c) parsing, by the computer processor, the patient data to an anatomical feature of the patient using a second trained classifier, wherein the parsing is based on a result of the categorizing; and (d) estimating, by the computer processor, the body surface model of the patient based on a result of the parsing. Systems for estimating a body surface model of a patient are described.

Estimating body surface models of patients

A method and system for segmenting multiple brain structures in 3D magnetic resonance (MR) images is disclosed. After intensity standardization of a 3D MR image, a meta-structure including center positions of multiple brain structures is detected in the 3D MR image. The brain structures are then individually segmented using marginal space learning (MSL) constrained by the detected meta-structure.

Method and system for segmentation of brain structures in 3D magnetic resonance images

A computer-implemented method for automatically calibrating an RGB-D sensor and an imaging device using a transformation matrix includes using a medical image scanner to acquire a first dataset representative of an apparatus attached to a downward facing surface of a patient table, wherein corners of the apparatus are located at a plurality of corner locations. The plurality of corner locations are identified based on the first dataset and the RGB-D sensor is used to acquire a second dataset representative of a plurality of calibration markers displayed on an upward facing surface of the patient table at the corner locations. A plurality of calibration marker locations are identified based on the second dataset and the transformation matrix is generated by aligning the first dataset and the second dataset using the plurality of corner locations and the plurality of calibration marker locations. Then, a translation is added to the transformation matrix corresponding to a maximum height associated with the apparatus.

Calibrating RGB-D sensors to medical image scanners

A method of assigning first localization data of a breast of a patient derived from first image data of the breast, the first image data being the result of a first radiological data acquisition process, to second localization data of the same breast derived from second image data, the second image data being the result of a second radiological data acquisition process, or vice versa. Thereby, the first localization data are assigned to the second localization data by intermediately mapping them into breast model data representing a patient-specific breast shape of the patient and then onto the second image data&#x2014;or vice versa, thereby deriving assignment data. An assignment system performs the above-described method.

Imaging device, assignment system and method for assignment of localization data

A method and system for the diagnosis of 3D images are disclosed, which significantly cuts the time required for the diagnosis. The 3D images are for example an image volume dataset of a magnetic resonance tomography system which is saved in an RIS or PACS system. In at least one embodiment, the diagnostic finding are partially automatically generated, and details of the position, size and change in pathological structures are compared to previous diagnostic findings are generated automatically. As a result of this automation the diagnostic work of radiologists is significantly reduced.

Computer-aided evaluation of an image dataset

Growing community of inventors

Bamberg, Germany

Michael Wels