Computer-implemented method for providing a cardiac motion classification based on Cardiac Magnetic Resonance, CMR, image data, wherein the CMR image data comprise a plurality of image frames, I(x, y, z, t), acquired for respective two-dimensional slices in at least one longitudinal direction, z, of the heart and for a plurality of times, t, the method including: - a myocardium segmentation step of inputting the plurality of image frames into two or more trained neural networks, applying the trained neural networks in parallel, and fusing an output of each of the trained neural networks into a single output indicating a segmentation, for each of the plurality of image frames, between a first portion indicating muscle tissue of the heart and a second portion indicating surrounding tissue of the heart muscle, and determining a corresponding mask of muscle tissue for the first portion; - a slice classification step of assigning each of the plurality of image frames in each slice, z, to an anatomic layer (170, 160, 150, 140, 130) of the heart; - a movement feature extraction and classification step of, for each of the masks and the corresponding anatomic layers (170, 160, 150, 140, 130), extracting a movement feature of the heart and classifying the movement feature into one of a number of pre-determined movement features; - an associating step of associating the classified movement feature with the corresponding layer for the cardiac motion classification, wherein the trained neural networks comprise two or more of: - a first neural network which considers the plurality of image frames, I(x, y, z, t), individually as input; - a second neural network which considers, for each of the plurality of image frames, I(x, y, z, t), also a previous time image frame, I(x, y, z, t-1), and a subsequent time image frame, I(x, y, z, t+1), as input; - a third neural network which considers, for each of the plurality of image frames, I(x, y, z, t), also a previous slice image frame, I(x, y, z-1, t), and a subsequent slice image frame, I(x, y, z+1, t), as input; - a fourth neural network which considers all of the plurality of image frames, I(x, y, z, t=1... N) for a given slice; and - a fifth neural network which considers all of the plurality of image frames, I(x, y, z=1... M, t) for a given time. - a myocardium segmentation step of inputting the plurality of image frames into two or more trained neural networks, applying the trained neural networks in parallel, and fusing an output of each of the trained neural networks into a single output indicating a segmentation, for each of the plurality of image frames, between a first portion indicating muscle tissue of the heart and a second portion indicating surrounding tissue of the heart muscle, and determining a corresponding mask of muscle tissue for the first portion; - a slice classification step of assigning each of the plurality of image frames in each slice, z, to an anatomic layer (170, 160, 150, 140, 130) of the heart; - a movement feature extraction and classification step of, for each of the masks and the corresponding anatomic layers (170, 160, 150, 140, 130), extracting a movement feature of the heart and classifying the movement feature into one of a number of pre-determined movement features; - an associating step of associating the classified movement feature with the corresponding layer for the cardiac motion classification, - a first neural network which considers the plurality of image frames, I(x, y, z, t), individually as input; - a second neural network which considers, for each of the plurality of image frames, I(x, y, z, t), also a previous time image frame, I(x, y, z, t-1), and a subsequent time image frame, I(x, y, z, t+1), as input; - a third neural network which considers, for each of the plurality of image frames, I(x, y, z, t), also a previous slice image frame, I(x, y, z-1, t), and a subsequent slice image frame, I(x, y, z+1, t), as input; - a fourth neural network which considers all of the plurality of image frames, I(x, y, z, t=1... N) for a given slice; and - a fifth neural network which considers all of the plurality of image frames, I(x, y, z=1... M, t) for a given time.