A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer ? s disease classification
A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer ? s disease classification
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DOI：
10.1016/j.mri.2021.02.001
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发表时间：
2021-02-26
影响因子：
2.5
通讯作者：
Long, Xiaojing
中科院分区：
文献类型：
--
作者：
Zhang, Jie;Zheng, Bowen;Long, Xiaojing
目的：阿尔茨海默病（AD）是一种进行性且不可逆的神经退行性疾病。近年来，机器学习方法已广泛应用于神经图像分析，以对AD进行定量评估和计算机辅助诊断或预测轻度认知障碍（MCI）向AD的转变。在本研究中，我们旨在开发一种新的深度学习方法来有效地检测或预测 AD。 材料和方法：我们提出了一种具有连接注意机制的密集连接的卷积神经网络，以学习大脑 MR 图像的多级特征以进行 AD 分类。我们使用密集连接的神经网络从预处理图像中提取多尺度特征，并应用连接注意机制来组合不同层特征之间的连接，以分层地将MR图像转换为更紧凑的高级特征。此外，我们将卷积运算扩展到 3D 以捕获 MRI 的空间信息。从每个 3D 卷积层提取的特征与前面所有不同关注层的特征相结合，最终用于分类。我们的方法在 ADNI 数据库中的 968 名受试者的基线 MRI 上进行了评估，以区分 (1) AD 与健康受试者，(2) MCI 转换者与健康受试者，以及 (3) MCI 转换者与非转换者。结果：所提出的方法在区分 AD 患者与健康对照方面达到了 97.35% 的准确度，区分 MCI 转换者与健康对照的准确度为 87.82%，MCI 转换者与非转换者的准确度为 78.79%。与最近研究中报道的一些神经网络和方法相比，我们提出的算法的分类性能名列前茅，并且在区分高风险转化为 AD 的 MCI 受试者方面有所提高。结论：深度学习技术提供了一个强大的工具来探索 MR 图像中微小但复杂的特征，这可能有助于 AD 的早期诊断和预测。
Purpose: Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. In recent years, machine learning methods have been widely used on analysis of neuroimage for quantitative evaluation and computer-aided diagnosis of AD or prediction on the conversion from mild cognitive impairment (MCI) to AD. In this study, we aimed to develop a new deep learning method to detect or predict AD in an efficient way.Materials and methods: We proposed a densely connected convolution neural network with connection-wise attention mechanism to learn the multi-level features of brain MR images for AD classification. We used the densely connected neural network to extract multi-scale features from pre-processed images, and connection wise attention mechanism was applied to combine connections among features from different layers to hierarchically transform the MR images into more compact high-level features. Furthermore, we extended the convolution operation to 3D to capture the spatial information of MRI. The features extracted from each 3D convolution layer were integrated with features from all preceding layers with different attention, and were finally used for classification. Our method was evaluated on the baseline MRI of 968 subjects from ADNI database to discriminate (1) AD versus healthy subjects, (2) MCI converters versus healthy subjects, and (3) MCI converters versus non-converters.Results: The proposed method achieved 97.35% accuracy for distinguishing AD patients from healthy control, 87.82% for MCI converters against healthy control, and 78.79% for MCI converters against non-converters. Compared with some neural networks and methods reported in recent studies, the classification performance of our proposed algorithm was among the top ranks and improved in discriminating MCI subjects who were in high risks of conversion to AD.Conclusions: Deep learning techniques provide a powerful tool to explore minute but intricate characteristics in MR images which may facilitate early diagnosis and prediction of AD.