Siemens Healthcare Gmbh

Siemens Healthineers Ag

Siemens Medical Solutions Usa, Inc.

Fernando Vega

Ali Kamen

Frank Sauer

Sasa Grbic

Stefan Thesen

Zhoubing Xu

Bin Lou

Carsten Thierfelder

Thilo Hannemann

Daniel Lerch

Robert Schneider

Thomas Boettger

Alexander Brost

Bruce S Spottiswoode

Sebastian Budz

Lisa Vallines

Gerhard Krämer

Selma Zehner

Sebastian Krueger

Volker Schaller

Bjoern Nolte

Nicolo Capobianco

Julian Rosenman

Systems and methods for automatically staging non-small cell lung cancer are provided. Patient data relating to a cancer of a patient is received. The patient data comprises one or more medical images and one or more biopsy reports. A T-stage of the cancer is determined based on a location and a size of one or more tumors of the cancer determined using the patient data. An N-stage of the cancer is determined by combining a metastasis evaluation of the cancer to regional lymph nodes determined from the one or more medical images and a metastasis evaluation of the cancer in the regional lymph nodes determined from the one or more biopsy reports. An M-stage of the cancer is determined based on a metastasis evaluation of the cancer to anatomical structures based on the patient data. The T-stage, the N-stage, and the M-stage are output.

Automatic staging of non-small cell lung cancer from medical imaging and biopsy reports

A computer-implemented method and a corresponding apparatus are provided for the provision of a two-dimensional visualization image having a plurality of visualization pixels for the visualization of a three-dimensional object represented by volume data for a user. Context information for the visualization is obtained by the evaluation of natural language and is taken into account in the visualization. The natural language can be in the form of electronic documents, which are assigned or can be assigned to the visualization process. In addition, the natural language can be in the form of a speech input of a user, during or after the visualization.

Method and apparatus for the visualization of three-dimensional objects

Risks of radiation-induced toxicity associated with a radiotherapy treatment of a target region of a patient are predicted. Data associated with a region of interest comprising the target region is received. The received data includes a predefined dose map of the radiotherapy treatment and pre-radiotherapy-treatment imaging data of the region of interest. A trained machine-learning algorithm is applied to the received data. The trained machine-learning algorithm generates at least one toxicity indicator based on the received data. The at least one toxicity indicator is indicative of the risks of the radiation-induced toxicity.

Radiation-induced toxicity and machine learning

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

A method and system for dose-optimized acquisition of a computed tomography (CT) scan of a target organ is disclosed. A localizer spiral CT scan is started at a beginning of a confidence range before a target organ. Real-time localizer scan images are automatically analyzed to predict a beginning location of the target organ based on the real-time localizer scan images. A diagnostic spiral CT scan is automatically started at the predicted beginning location of the target organ. Real-time diagnostic scan images are automatically analyzed to predict an end location of the target organ where full coverage of the target organ will be reached. The diagnostic spiral CT scan is automatically stopped in response to reaching the predicted end location of the target organ. A 3D profile can be acquired using a 3D camera and used to determine the confidence range before the target organ.

Method and system for dose-optimized computed tomography scanning of a target organ

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Erlangen, Germany

Fernando Vega