  Brain MRI segmentation results should always undergo a quality control (QC)
process, since automatic segmentation tools can be prone to errors. In this
work, we propose two deep learning-based architectures for performing QC
automatically. First, we used generative adversarial networks for creating
error maps that highlight the locations of segmentation errors. Subsequently, a
3D convolutional neural network was implemented to predict segmentation
quality. The present pipeline was shown to achieve promising results and, in
particular, high sensitivity in both tasks.
