Cardiac magnetic resonance (CMR) is considered the gold standard for evaluating cardiac function. Tools for automatic segmentation already exist in the clinical context. To bring the benefits of automatic segmentation to preclinical research, we use a deep learning based model. We demonstrate that for training small data sets with parameters deviating from the clinical situation (high resolution images of porcine hearts acquired at 7T in this case) are sufficient to achieve good correlation with manual segmentation. We obtain DICE-scores of 0.87 (LV) and 0.85 (myocardium) and find high agreement of the calculated functional parameters (Pearson‘s r between 0.95 and 0.99).
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