Siemens Healthcare Gmbh

Siemens Healthineers Ag

Ramyar Biniazan

Ludwig Ritschl

Andreas Fieselmann

Christian Huemmer

Thomas Mertelmeier

Steffen Kappler

Julia Wicklein

Wen Man He

Stephan Dwars

Manasi Datar

Christian Hümmer

Peter Zerfass

In a method, comparison features are extracted from labeled reference image data. Features are also extracted from the image data. A statistical comparison of the comparison features with the features then takes place. On the basis of the statistical comparison and a quality criterion, the quality of the AI-based result data is determined. A method for correcting result data is additionally described. Furthermore, a method for AI-based acquisition of result data on the basis of measured examination data is described. Also described is a validation entity. An entity for correcting result data is additionally described. Furthermore, an entity for acquiring result data is described. Also described is a medical imaging entity.

Validation of AI-based result data

A method is for generating a synthetic mammogram. In an embodiment, the method incudes acquisition of a plurality of projection data sets at a plurality of projection angles; and generation of at least one synthetic mammogram with an image property essentially equivalent to a conventional full-field digital mammography acquisition based on several projection data sets.

Synthetic mammogram with imprinted image impression

A computer program, a system and a method for normalizing medical images from a type of image acquisition device using a machine learning unit are disclosed. An embodiment of the method includes receiving a set of image data with images; decomposing each of the images of the set of images into components by incorporating at least information from different settings of the image acquisition device-specific image processing algorithms; and normalizing each of the components via a machine learning unit by processing at least information from the different settings of the image acquisition device-specific processing algorithms to provide a set of normalized images with a relatively decreased variability score.

Image normalization increasing robustness of machine learning applications for medical images

A method is for monitoring a tissue removal taking place via an X-ray imaging system. The method includes acquiring first X-ray projection scan data, influenced by a contrast medium, of the breast tissue situated in an image field of the X-ray imaging system. The contrast medium-influenced first X-ray projection scan data is generated with X-ray photons having a photon energy above an absorption edge of the contrast medium used. A first image data set is then reconstructed based upon the acquired X-ray projection scan data. Finally, based upon the reconstructed first image data set, the method includes determining a current position of the lesion, in relation to a reference coordinate system.

Method for monitoring a tissue removal by means of an x-ray imaging system

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Nuremberg, Germany

Ramyar Biniazan