Real time MRI is a promising modality for the measurement or myocardial function without the need for breath-holding or ECG triggering. To enable the quantitative assessment of non-temporally aligned image slices representing multiple heartcycles we present an automatic image analysis approach based on a segmentation using the U-net convolutional neural network model. The comparison of segmentation masks with reference data show a very good DICE coefficient of 0.94. The comparison of quantitative results achieved based on the expert-corrected conventional segmentation shows promising results and suggests that further improvement can be achieved through parameter adaptation.
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