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In this work we present a machine learning model to segment long axis magnetic resonance images of the left ventricle (LV) and address the challenges encountered when, in doing so, a small training dataset is used. Our approach is based on a heart locator and an anatomically guided UNet model in which the UNet is followed by a B-Spline head to condition training. The model is developed using transfer learning, which enabled the training and testing of the proposed strategy from a small swine dataset. The segmented LV cavity and myocardium in the long axis view show good agreement with ground truth segmentations at different cardiac phases based on the Dice similarity coefficient. In addition the model provides a measure of segmentations' uncertainty, which can then be incorporated while developing LV computational models and indices of cardiac performance based on the segmented images. Finally, several challenges related to long axis, as opposed to short axis, image segmentation are highlighted, including proposed solutions.