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The bottleneck of convolutional neural networks (CNN) for medical imaging isthe number of annotated data required for training. Manual segmentation isconsidered to be the "gold-standard". However, medical imaging datasets withexpert manual segmentation are scarce as this step is time-consuming andexpensive. We propose in this work the use of what we refer to as silverstandard masks for data augmentation in deep-learning-based skull-strippingalso known as brain extraction. We generated the silver standard masks usingthe consensus algorithm Simultaneous Truth and Performance Level Estimation(STAPLE). We evaluated CNN models generated by the silver and gold standardmasks. Then, we validated the silver standard masks for CNNs training in onedataset, and showed its generalization to two other datasets. Our resultsindicated that models generated with silver standard masks are comparable tomodels generated with gold standard masks and have better generalizability.Moreover, our results also indicate that silver standard masks could be used toaugment the input dataset at training stage, reducing the need for manualsegmentation at this step.

Silver Standard Masks for Data Augmentation Applied to Deep-Learning-Based Skull-Stripping