Deep learning techniques have proved their robustness in solving medical image analysis problems. This study proposes a conservative approach to benefit from the use of these methods to incrementally improve the performance of a well-established brain segmentation method. For this purpose, convolutional neural networks are trained to perform a reliable skull-stripping, based on weak labels of the original algorithm. The performance of the new pipeline is evaluated in a large cohort of dementia patients and healthy controls. The results present significant improvements in reproducibility and computation speed, while preserving accuracy and power of discrimination between groups.
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