Convolutional neural networks for MR image segmentation require a large amount of labelled data. Nevertheless, medical image datasets with expert manual segmentation, which is usually the gold standard for that task, are scarce as this step is both time-consuming and labor intensive. We propose a deep-learning-based skull-stripping (SS) method trained using data provided by consensus-based data augmentation through silver standard masks. Silver standard masks are generated using Simultaneous Truth and Performance Level Estimation (STAPLE) consensus algorithm. Our results indicate comparable performance to state-of-the-art-methods, but computationally effcient even under CPU-based processing.
