In this paper, we describe a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours. The network was trained and evaluated on a large dataset (n = 11,480), obtained by combining data from more than a hundred different sites, and also evaluated on another completely held-out dataset (n = 418). The network was trained using a novel spike-and-slab dropout-based variational inference approach. We show that, on these datasets, the proposed Bayesian DNN outperforms previously proposed methods, in terms of the similarity between the segmentation predictions and the FreeSurfer labels, and the usefulness of the estimate uncertainty of these predictions. In particular, we demonstrated that the prediction uncertainty of this network at each voxel is a good indicator of whether the network has made an error and that the uncertainty across the whole brain can predict the manual quality control ratings of a scan. The proposed Bayesian DNN method should be applicable to any new network architecture for addressing the segmentation problem.

中文翻译：

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使用贝叶斯深度神经网络了解大脑分割中的知识

在本文中，我们描述了一种贝叶斯深度神经网络 (DNN)，用于在几分钟而不是几小时内预测结构 MRI 体积的 FreeSurfer 分割。该网络在一个大型数据集（n = 11,480）上进行了训练和评估，该数据集是通过组合来自一百多个不同站点的数据而获得的，并且还在另一个完全保留的数据集（n = 418）上进行了评估。该网络使用一种新颖的基于尖峰和平板丢失的变分推理方法进行训练。我们表明，在这些数据集上，就分割预测和 FreeSurfer 标签之间的相似性以及这些预测的估计不确定性的有用性而言，所提出的贝叶斯 DNN 优于先前提出的方法。特别是，我们证明了该网络在每个体素处的预测不确定性可以很好地指示网络是否犯了错误，并且整个大脑的不确定性可以预测扫描的手动质量控制评级。所提出的贝叶斯 DNN 方法应该适用于任何新的网络架构来解决分割问题。