Training of neural networks for segmentation of CMR images requires large amounts of labeled data and network generalization is biased by training data characteristics. We used a specialized network to label a heterogeneous, publicly available dataset of 1140 cine images with known left-ventricular volumes. We evaluated the performance of this network using true and predicted volumes and trained another neural network on subjects with high prediction accuracy using extensive data augmentation. The resulting network outperforms the original one on the full dataset, even on subgroups where the original network fails, indicating great generalization and thus suitability for transfer learning applications.
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