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Abstract #2731

2D Single Plane Big Data Convolutional Neural Network for Skull-Stripping

Oeslle Lucena1, Roberto Souza2, Richard Frayne2, Letícia Rittner1, and Roberto Lotufo1

1University of Campinas, Campinas, Brazil, 2University of Calgary, Calgary, AB, Canada

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.

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