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Brain MRI segmentation results should always undergo a quality control (QC)process, since automatic segmentation tools can be prone to errors. In thiswork, we propose two deep learning-based architectures for performing QCautomatically. First, we used generative adversarial networks for creatingerror maps that highlight the locations of segmentation errors. Subsequently, a3D convolutional neural network was implemented to predict segmentationquality. The present pipeline was shown to achieve promising results and, inparticular, high sensitivity in both tasks.

A deep learning-based pipeline for error detection and quality control of brain MRI segmentation results