Possessing a balanced dataset to train the segmentation models used in clinical products is hugely important to enhance the performance in any given subset of patients. Due to demographics and uneven distribution of conditions of the subjects, this is not usually the case. In this work, we propose a novel method that synthetically balances a training dataset by applying deformations to an atlas. Once the atlas is processed, we obtain different slice cuts from it and apply style transfer to make it appear as a real short-axis MRI scan. We then add those synthetic scans to our training set for the segmentation network. We found that using synthetic scans to balance the dataset resulted in up to a 0.05 increase in the DICE score.