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首页> 美国卫生研究院文献>Frontiers in Neuroscience >Classification of Subcortical Vascular Cognitive Impairment Using Single MRI Sequence and Deep Learning Convolutional Neural Networks
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Classification of Subcortical Vascular Cognitive Impairment Using Single MRI Sequence and Deep Learning Convolutional Neural Networks

机译：使用单个MRI序列和深度学习卷积神经网络对皮层下血管认知障碍进行分类

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摘要

Deep learning has great potential for imaging classification by extracting low to high-level features. Our aim was to train a convolutional neural network (CNN) with single T2-weighted FLAIR sequence to classify different cognitive performances in patients with subcortical ischemic vascular disease (SIVD). In total, 217 patients with SIVD [including 52 with vascular dementia (VaD), 82 with vascular mild cognitive impairment (VaMCI), and 83 with non-cognitive impairment (NCI)] and 46 matched healthy controls (HCs) underwent MRI scans and neuropsychological assessments. 2D and 3D CNNs were trained to classify VaD, VaMCI, NCI, and HCs based on FLAIR data. For 3D-based model, the loss curves of the training set approached 0.017 after about 20 epochs, while the curves of the testing set maintained at about 0.114. The accuracy of training set and testing set reached 99.7 and 96.9% after about 30 and 35 epochs, respectively. However, the accuracy of the 2D-based model was only around 70%, which performed significantly worse than 3D-based model. This experiment suggests that deep learning is a powerful and convenient method to classify different cognitive performances in SIVD by extracting the shift and scale invariant features of neuroimaging data with single FLAIR sequence. 3D-CNN is superior to 2D-CNN which involves clinical evaluation with MRI multiplanar reformation or volume scanning.
机译：深度学习通过提取从低到高的特征在图像分类方面具有巨大潜力。我们的目的是训练具有单个T2加权FLAIR序列的卷积神经网络（CNN），以对皮层下缺血性血管病（SIVD）患者的不同认知表现进行分类。总计217例SIVD患者[包括52例患有血管性痴呆（VaD），82例患有轻度认知障碍（VaMCI）和83例非认知障碍（NCI）]和46例健康对照（HCs）接受了MRI扫描和神经心理学评估。训练了2D和3D CNN，以便根据FLAIR数据对VaD，VaMCI，NCI和HC进行分类。对于基于3D的模型，训练集的损失曲线在大约20个纪元后接近0.017，而测试集的曲线维持在大约0.114。训练集和测试集的准确性分别在大约30和35个纪元后达到了99.7％和96.9％。但是，基于2D的模型的准确性仅为70％左右，其性能明显低于基于3D的模型。该实验表明，深度学习是一种通过使用单个FLAIR序列提取神经影像数据的移位和尺度不变特征来对SIVD中不同认知表现进行分类的强大而便捷的方法。 3D-CNN优于2D-CNN，后者涉及MRI多平面重建或体积扫描的临床评估。

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