CNRS, University of Bordeaux, 'Institut de Mathématiques de Bordeaux' (IMB), UMR5251, F-33400 Talence, France.
ITACA, Universitat Politècnica de València, Camino de Vera, s/n, 46022, Valencia, Spain.
Phys Med Biol. 2020 Nov 17;65(22):225022. doi: 10.1088/1361-6560/abb6be.
Affine registration of one or several brain image(s) onto a common reference space is a necessary prerequisite for many image processing tasks, such as brain segmentation or functional analysis. Manual assessment of registration quality is a tedious and time-consuming task, especially in studies comprising a large amount of data. Automated and reliable quality control (QC) becomes mandatory. Moreover, the computation time of the QC must be also compatible with the processing of massive datasets. Therefore, automated deep neural network approaches have emerged as a method of choice to automatically assess registration quality. In the current study, a compact 3D convolutional neural network, referred to as RegQCNET, is introduced to quantitatively predict the amplitude of an affine registration mismatch between a registered image and a reference template. This quantitative estimation of registration error is expressed using the metric unit system. Therefore, a meaningful task-specific threshold can be manually or automatically defined in order to distinguish between usable and non-usable images. The robustness of the proposed RegQCNET is first analyzed on lifespan brain images undergoing various simulated spatial transformations and intensity variations between training and testing. Secondly, the potential of RegQCNET to classify images as usable or non-usable is evaluated using both manual and automatic thresholds. During our experiments, automatic thresholds are estimated using several computer-assisted classification models (logistic regression, support vector machine, Naive Bayes and random forest) through cross-validation. To this end we use an expert's visual QC estimated on a lifespan cohort of 3953 brains. Finally, the RegQCNET accuracy is compared to usual image features such as image correlation coefficient and mutual information. The results show that the proposed deep learning QC is robust, fast and accurate at estimating affine registration error in the processing pipeline.
将一个或多个脑图像配准到共同参考空间是许多图像处理任务（如脑分割或功能分析）的必要前提。手动评估配准质量是一项繁琐且耗时的任务，特别是在包含大量数据的研究中。自动化和可靠的质量控制（QC）变得必不可少。此外，QC 的计算时间也必须与大规模数据集的处理兼容。因此，自动化的深度神经网络方法已成为自动评估配准质量的首选方法。在当前的研究中，引入了一种紧凑的 3D 卷积神经网络，称为 RegQCNET，用于定量预测配准图像与参考模板之间的仿射配准不匹配的幅度。这种配准误差的定量估计使用度量单位系统表示。因此，可以手动或自动定义有意义的特定于任务的阈值，以区分可用和不可用的图像。首先在训练和测试之间经历各种模拟空间变换和强度变化的寿命期脑图像上分析所提出的 RegQCNET 的稳健性。其次，使用手动和自动阈值评估 RegQCNET 将图像分类为可用或不可用的能力。在实验过程中，通过交叉验证使用几种计算机辅助分类模型（逻辑回归、支持向量机、朴素贝叶斯和随机森林）来估计自动阈值。为此，我们使用了专家对 3953 个大脑的寿命期队列的视觉 QC 进行了估计。最后，将 RegQCNET 的准确性与图像相关系数和互信息等常用图像特征进行了比较。结果表明，所提出的深度学习 QC 在处理管道中能够快速、准确地估计仿射配准误差，具有较高的准确性。
