Abstract: Techniques for processing one or more frames of an angiogram are disclosed. The processing may take place during or after an angiography exam. The one or more frames of the angiogram are acquired during the angiography exam. The one or more frames are processed to determine, based on at least one pre-defined criterion, whether the angiogram at least comprises one frame with a diagnostic value among the one or more frames. If the angiogram comprises at least one frame with the diagnostic value, based on the angiogram, a score quantifying the diagnostic value of the angiogram is determined using a trained machine-learning (ML) algorithm. Techniques for processing, e.g., ranking/sorting, multiple angiograms associated with an anatomical region of interest of a patient are also provided, by which a respective score for each of the multiple angiograms is determined using the techniques for processing one or more frames of an angiogram.

Abstract: Angiography angles are determined. Patient information and target vessel information are obtained, wherein the patient information defines individual medical information of a patient and wherein the target vessel information defines at least one target vessel to be imaged. At least one angiography angle is determined based on the patient information and the target vessel information. Angiograms obtained using the at least one angiography angle are analyzed to determine a vessel coverage of the target vessel and based on the vessel coverage determines additional angiography angles.

Abstract: Systems and methods for determining corresponding locations of points of interest in a plurality of input medical images are provided. A plurality of input medical images comprising a first input medical image and one or more additional input medical images is received. The first input medical image identifies a location of a point of interest. A set of features is extracted from each of the plurality of input medical images. Features between each of the sets of features are related using a machine learning based relational network. A location of the point of interest in each of the one or more additional input medical images that corresponds to the location of the point of interest in the first input medical image is identified based on the related features. The location of the point of interest in each of the one or more additional input medical images is output.

Abstract: 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.

Abstract: Method and system for image registration or image segmentation. The method includes receiving an image which is to be processed by a first machine-learning model to perform, for example, image registration or segmentation, and using a second machine-learning model to determine if the received image is of a quality suitable for the first machine-learning model to act upon.

Abstract: In a system and method for analyzing images, an input image is provided to a computer and is processed therein with a first deep learning model so as to generate an output result for the input image; and applying a second deep learning model is applied to the input image to generate an output confidence score that is indicative of the reliability of any output result from the first deep learning model for the input image.

Abstract: Method and system for image registration or image segmentation. The method includes receiving an image which is to be processed by a first machine-learning model to perform, for example, image registration or segmentation, and using a second machine-learning model to determine if the received image is of a quality suitable for the first machine-learning model to act upon.

Abstract: In a system and method for analyzing images, an input image is provided to a computer and is processed therein with a first deep learning model so as to generate an output result for the input image; and applying a second deep learning model is applied to the input image to generate an output confidence score that is indicative of the reliability of any output result from the first deep learning model for the input image.