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arXiv · 2005.13987

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
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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.

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Irene Brusini, Daniel Ferreira Padilla, José Barroso, Ingmar Skoog, Örjan Smedby, Eric Westman, Chunliang Wang. 2020-05-28. A deep learning-based pipeline for error detection and quality control of brain MRI segmentation results. https://arxiv.org/abs/2005.13987

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