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

25 Jan 2020, 12:35 (edited 25 Jun 2020)MIDL 2020 Conference Blind SubmissionReaders: Everyone
  • Abstract: 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.
  • Paper Type: methodological development
  • Track: short paper
  • Keywords: brain MRI, segmentation, quality control, GANs, 3D CNN
  • TL;DR: The proposed pipeline combines GANs and a 3D CNN to automatically perform quality control of brain MRI segmentation results, achieving promising results.
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