S198 - A deep learning-based pipeline for error detection and quality control of brain MRI segmentation results
Irene Brusini, Daniel Ferreira Padilla, José Barroso, Ingmar Skoog, Örjan Smedby, Eric Westman, Chunliang Wang
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.
Poster Session #5 - 9:30 - 11:00 UTC-4 (Wednesday)