Bayesian QuickNAT: Model uncertainty in deep whole-brain segmentation for structure-wise quality control
Bayesian QuickNAT: Model uncertainty in deep whole-brain segmentation for structure-wise quality control
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DOI：
10.1016/j.neuroimage.2019.03.042
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发表时间：
2019-07-15
期刊：
影响因子：
5.7
通讯作者：
Wachinger, Christian
中科院分区：
文献类型：
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作者：
Roy, Abhijit Guha;Conjeti, Sailesh;Wachinger, Christian
我们引入贝叶斯 QuickNAT，用于 MRI T1 扫描全脑分割的自动质量控制。除了贝叶斯全卷积神经网络之外，我们还提出了分割不确定性的固有测量方法，可以对每个大脑结构进行质量控制。为了估计模型不确定性，我们遵循贝叶斯方法，其中通过在测试时保持 dropout 层处于活动状态来生成后验分布的蒙特卡罗 (MC) 样本。 MC 样本的熵提供了体素模型的不确定性图，而 MC 预测的期望提供了最终的分割。除了体素方面的不确定性之外，我们还引入了四个指标来量化质量控制分割中的结构方面的不确定性。我们报告了四个样本外数据集的实验，其中包括不同的年龄范围、病理学和成像伪影。所提出的结构方面的不确定性度量与通过手动注释估计的 Dice 分数高度相关，因此提供了分割质量的固有度量。特别是，所有 MC 样本的并集的交集是 Dice 分数的合适代理。除了扫描级别的质量控制之外，我们建议将结构方面的不确定性纳入对大型数据存储库进行可靠的组分析的置信度度量。我们设想引入的不确定性指标将有助于评估基于自动化深度学习的大规模人口研究分割方法的保真度，因为它们在处理大型数据存储库时实现了自动化质量控制和分组分析。
We introduce Bayesian QuickNAT for the automated quality control of whole-brain segmentation on MRI T1 scans. Next to the Bayesian fully convolutional neural network, we also present inherent measures of segmentation uncertainty that allow for quality control per brain structure. For estimating model uncertainty, we follow a Bayesian approach, wherein, Monte Carlo (MC) samples from the posterior distribution are generated by keeping the dropout layers active at test time. Entropy over the MC samples provides a voxel-wise model uncertainty map, whereas expectation over the MC predictions provides the final segmentation. Next to voxel-wise uncertainty, we introduce four metrics to quantify structure-wise uncertainty in segmentation for quality control. We report experiments on four out-of-sample datasets comprising of diverse age range, pathology and imaging artifacts. The proposed structure-wise uncertainty metrics are highly correlated with the Dice score estimated with manual annotation and therefore present an inherent measure of segmentation quality. In particular, the intersection over union over all the MC samples is a suitable proxy for the Dice score. In addition to quality control at scan-level, we propose to incorporate the structure-wise uncertainty as a measure of confidence to do reliable group analysis on large data repositories. We envisage that the introduced uncertainty metrics would help assess the fidelity of automated deep learning based segmentation methods for large-scale population studies, as they enable automated quality control and group analyses in processing large data repositories.