论文标题
基于深度学习的管道，用于错误检测和大脑MRI分割结果的质量控制
A deep learning-based pipeline for error detection and quality control of brain MRI segmentation results
论文作者
论文摘要
大脑MRI分割结果应始终进行质量控制（QC）过程，因为自动分割工具可能容易出错。在这项工作中，我们提出了两种基于深度学习的体系结构，以自动执行QC。首先，我们使用生成的对抗网络来创建错误图，以突出显示分割错误的位置。随后，实施了3D卷积神经网络以预测细分质量。目前的管道已显示出有希望的结果，尤其是在这两个任务中都高灵敏度。
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.
