Abstract: An apparatus, system and method for detecting anomalies in a grid are disclosed. The method includes transforming data acquired from the grid based on at least one of a Fast Fourier Transformation and a spectrogram of the data, wherein the data acquired includes data associated with at least one of grid voltage, grid current, grid frequency, phase; fitting the data using a fitting function initialized using at least the transformed data, wherein the fitting function includes at least one of a sinusoidal function; or generating a lower representation of at least one of the data acquired and the transformed data; and detecting the anomaly in the grid based on at least one outlier detected in the fitted data or the lower representation of data using at least one of a parameter deviation and the similarity index.

Abstract: One or more example embodiments of the invention relates in one aspect to a computer-implemented method for providing a stroke information. The method includes receiving examination data, the examination data comprising computed tomography imaging data of an examination area of a patient, the examination area of the patient comprising at least one brain region, the at least one brain region being affected by a stroke; adjusting a causal model based on the computed tomography imaging data to obtain an adjusted causal model, wherein the adjusted causal model models a first variable as a first cause for an appearance of the examination area of the patient; receiving a first value for the first variable; generating the stroke information based on the adjusted causal model and the first value for the first variable; and providing the stroke information.

Abstract: A method for generating training data for training a deep learning algorithm, comprising: receiving medical imaging data of an examination area including a first part and a second part of a symmetric organ; splitting the medical imaging data along a symmetry plane or a symmetry axis into a first dataset and a second dataset, wherein the first dataset includes the medical imaging data of the first part and the second dataset includes the medical imaging data of the second part; mirroring the second dataset along the symmetry plane or the symmetry axis; generating the training data by stacking the first dataset and the mirrored second dataset; and providing the training data.

Abstract: A computer implemented method maps a three-dimensional branched tubular structure depicted in a three-dimensional image data set into at least one two-dimensional image plane.

Abstract: A computer-implemented segmentation method for segmenting a core and a penumbra in a four-dimensional computed tomography perfusion dataset of ischemic tissue in an image region of a patient, includes determining at least one parameter map for at least one perfusion parameter from the computed tomography perfusion dataset; and using the at least one parameter map and the computed tomograph perfusion dataset as input data to a trained function to determine output data, the output data including segmentation information of the penumbra and the core.

Abstract: A method and system are for analyzing a plurality of interconnected blood vessels, preferably for providing output data for the analysis of a plurality of interconnected blood vessels, e.g. for vessel segmentation, vessel labeling, and vessel occlusion detection. The output data include a graphical representation of a vessel tree model of a patient. The method of an embodiment, in the most general terms includes receiving input data, e.g. via an interface; generating output data by applying algorithmic operations to the input data; and providing the output data, e.g. via an interface.