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

arXiv: https://arxiv.org/abs/2005.13987 · HTML: https://mlsanity.com/paper/2005.13987/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2020.

## Built on by (most cited first, 2 of 2)

- [A Decoupled Uncertainty Model for MRI Segmentation Quality Estimation](https://mlsanity.com/paper/2109.02413/) (2021)
- [Automatic quality control framework for more reliable integration of machine learning-based image segmentation into medical workflows](https://mlsanity.com/paper/2112.03277/) (2021)

## Abstract (arXiv)

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.