Abstract: A computer-implemented method, comprising: receiving input data including a medical image and an in-image annotation; applying a first function to the input data to determine a relevance value of pixels in the image and a relevance map; applying a second function to the medical image to generate a de-identified medical image; applying a trained function to the medical image and the de-identified medical image to determine a first property in the medical image and a second property in the de-identified medical image; applying a comparison function to the first property and the second property to determine a similarity value, wherein in response to the similarity value being below a similarity threshold, the relevance map is adjusted and the applying of the second function, the applying of the trained function and the applying of the comparison function are repeated; and providing the de-identified medical image and the in-image annotation.

Abstract: One or more example embodiments of the present invention relates to Computer-implemented method for providing a positioning score regarding a positioning of an examining region in an X-ray image, comprising receiving input data, the input data comprising an X-ray image including the examining region; applying a first trained function to the input data to detect at least one region of interest in the X-ray image and to generate a heatmap comprising the at least one region of interest; applying a second trained function to the input data and the heatmap to generate an individual score for each of the at least one region of interest and to generate a score-weighted heatmap based on the at least one region of interest and the individual scores; applying a third trained function to the input data and the score-weighted heatmap to generate a positioning score; and providing the positioning score.

Abstract: A computer-implemented method comprises: receiving input data, wherein the input data includes the X-ray image dataset, which includes an X-ray image and first metadata; applying a trained function to the input data to generate output data, wherein the output data includes second metadata, and wherein the first metadata and the second metadata are compared; and providing the output data, wherein the first metadata are confirmed in case the first metadata and the second metadata agree, or the first metadata are suggested to be corrected with the second metadata in case the first metadata and the second metadata do not agree.

Abstract: 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.

Abstract: 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.

Abstract: A method comprises: applying a first trained function to first input data to generate first output data, the first output data including first key elements; receiving second input data, the second input data being an X-ray image of an examination region acquired using a first collimation region; applying a second trained function to the second input data to generate second output data, the second output data including second key elements; receiving third input data in response to an incomplete set of second key elements, the third input data including the second key elements and an X-ray image of the examination region acquired using the first collimation region; applying a third trained function to the third input data to generate third output data, the third output data including an estimated third key element to complete the set of second key elements; and providing a complete set of second key elements.

Abstract: One or more example embodiments relates to a computer-implemented method for providing key elements of the examination region in an X-ray image.

Abstract: According to a method for correcting a 2D measurement value is described, 2D image data of an examination object is received. Landmarks in the 2D image data are detected, and 2D positions of the landmarks are calculated. A corrected measurement value of the examination object is predicted, using a trained model, which depends on the received 2D image data, the estimated 2D positions of the landmarks and a reference parameter of a reference 3D orientation of the examination object.

Abstract: One or more example embodiments provides a system and a method for differentiating a tissue of interest from another part of a medical scanner image, in particular pectoral muscle tissue from breast tissue in an X-ray mammography image. The method comprises providing a medical scanner image; inputting input data into a trained artificial neural network, the input data being based on the provided medical scanner image; generating, by the trained artificial neural network, output data based on the input data, the output data indicating a one-dimensional borderline between at least a part of the tissue of interest and the at least one other part of the medical scanner image; and outputting an output signal comprising or based on the generated output data.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.