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

2020/05/28 by Irene Brusini, Brusini, Irene, José Barroso +10
Computer Science · Medicine · #FOS: Electrical engineering #Generative Adversarial Networks and Image Synthesis #Image and Video Processing (eess.IV) #Medical Image Segmentation Techniques #Radiomics and Machine Learning in Medical Imaging #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2005.13987

openalex publication_date 2020/05/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Related

• QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Prediction
• Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy
• Automatic quality control framework for more reliable integration of machine learning-based image segmentation into medical workflows
• Multi-modal segmentation of 3D brain scans using neural networks
• End-to-end learning of brain tissue segmentation from imperfect labeling