Siemens Healthcare Gmbh

Siemens Healthineers Ag

Philipp Hölzer

Vivek Kumar Singh

Ankur Kapoor

Bogdan Georgescu

Shaohua Kevin Zhou

Sebastian Schmidt

Sasa Grbic

Grzegorz Soza

Christian Hofmann

Zhoubing Xu

Andreas Wimmer

Razvan Ionasec

Christopher Jude Amies

Björn Kreisler

Jonathan Sperl

Mark-Aleksi Keller-Reichenbecher

André Ritter

René Kartmann

Eva Eibenberger

Nico Kaiser

Amitkumar Bhupendrakumar Shah

Richard Frank

Peng Hao

A system and method for identifying abnormal medical images. The system can be configured to receive a medical image, segment an anatomical structure from the medical image to define a segmented dataset, register the segmented dataset to a baseline dataset defining a normal anatomical structure, classify, by an abnormality classifier, whether the anatomical structure within the medical image as either abnormal or normal, wherein the abnormality classifier comprises a machine learning algorithm trained to distinguish between normal and abnormal versions of the anatomical structure in medical images, and based on whether the anatomical structure can be segmented from the medical image, whether the segmented dataset can be registered to the baseline dataset, or a classification associated with the medical image output by the abnormality classifier, flagging the medical image as either normal or abnormal.

AI-based image analysis for the detection of normal images

Systems and methods for a reduced interaction CT scanning workflow. A sensor is used to capture an image of a patient on the table. Scan parameters are automatically set. A full CT scan is performed without a scout scan. During the full CT scan, the scan parameters are adjusted based on the raw CT measurements from the full CT scan. A radiology report is automatically generated from the results of the full CT scan.

Reduced interaction CT scanning

Systems and methods are provided for automatic detection and quantification for traumatic bleeding. Image data is acquired using a full body dual energy CT scanner. A machine-learned network detects one or more bleeding areas on a bleeding map from the dual energy CT scan image data. A visualization is generated from the bleeding map. The predicted bleeding areas are quantified, and a risk value is generated. The visualization and risk value are presented to an operator.

Detection and quantification for traumatic bleeding using dual energy computed tomography

A framework for sensor-based patient treatment support. In accordance with one aspect, one or more sensors are used to acquire sensor data of one or more objects of interest. The sensor data is then automatically interpreted to generate processing results. One or more actions may be triggered based on the processing results to support treatment of a patient, including supporting medical scanning of the patient.

Sensor-based patient treatment support

Growing community of inventors

Baltimore, MD, United States of America

Philipp Hölzer