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Automated White Matter Hyperintensity Segmentation Using Bayesian Model Selection: Assessment and Correlations with Cognitive Change

机译：使用Bayesian模型选择自动白品超强度分割：评估和与认知变化的相关性

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摘要

Accurate, automated white matter hyperintensity (WMH) segmentations are needed for large-scale studies to understand contributions of WMH to neurological diseases. We evaluated Bayesian Model Selection (BaMoS), a hierarchical fully-unsupervised model selection framework for WMH segmentation. We compared BaMoS segmentations to semi-automated segmentations, and assessed whether they predicted longitudinal cognitive change in control, early Mild Cognitive Impairment (EMCI), late Mild Cognitive Impairment (LMCI), subjective/significant memory concern (SMC) and Alzheimer's (AD) participants. Data were downloaded from the Alzheimer's disease Neuroimaging Initiative (ADNI). Magnetic resonance images from 30 control and 30 AD participants were selected to incorporate multiple scanners, and were semi-automatically segmented by 4 raters and BaMoS. Segmentations were assessed using volume correlation, Dice score, and other spatial metrics. Linear mixed-effect models were fitted to 180 control, 107 SMC, 320 EMCI, 171 LMCI and 151 AD participants separately in each group, with the outcomes being cognitive change (e.g. mini-mental state examination; MMSE), and BaMoS WMH, age, sex, race and education used as predictors. There was a high level of agreement between BaMoS' WMH segmentation volumes and a consensus of rater segmentations, with a median Dice score of 0.74 and correlation coefficient of 0.96. BaMoS WMH predicted cognitive change in: control, EMCI, and SMC groups using MMSE; LMCI using clinical dementia rating scale; and EMCI using Alzheimer's disease assessment scale-cognitive subscale (p < 0.05, all tests). BaMoS compares well to semi-automated segmentation, is robust to different WMH loads and scanners, and can generate volumes which predict decline. BaMoS can be applicable to further large-scale studies.
机译：大规模研究需要准确，自动白质超强度（WMH）分割，以了解WMH对神经疾病的贡献。我们评估了贝叶斯模型选择（BAMOS），是WMH分段的分层完全无监督的模型选择框架。我们将BAMOS分段与半自动分割进行比较，并评估他们是否预测了对照，早期性认知障碍（EMCI），后期轻度认知障碍（LMCI），主观/重要记忆关注（SMC）和Alzheimer（AD）的纵向认知变化。参与者。数据从Alzheimer的神经影像学倡议（ADNI）下载。选择来自30个控制和30名广告参与者的磁共振图像以包含多个扫描仪，并由4个评分器和BAMOS进行半自动分段。使用体积相关，骰子得分和其他空间指标进行分段进行分段。线性混合效果模型适用于180个控制，107个SMC，320个EMCI，171 LMCI和151名广告参与者，其结果是认知变化（例如迷你精神状态检查; MMSE），和BAMOS WMH，年龄，性别，种族和教育用作预测因素。 BAMOS的WMH分割卷与评估分割的共识之间存在高水平的协议，中位数骰子得分为0.74，相关系数为0.96。 BAMOS WMH预测认知变化：使用MMSE的控制，EMCI和SMC组; LMCI使用临床痴呆评定量表;和EMCI使用阿尔茨海默病评估规模 - 认知亚级（P <0.05，所有测试）。 BAMOS比较良好的半自动分段，对不同的WMH负载和扫描仪具有鲁棒，可以生成预测下降的卷。 BAMOS可以适用于进一步的大规模研究。

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