Deep sequence modelling for Alzheimer's disease detection using MRI
Deep sequence modelling for Alzheimer's disease detection using MRI
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DOI：
10.1016/j.compbiomed.2021.104537
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发表时间：
2021-06-09
影响因子：
7.7
通讯作者：
Chiong, Raymond
中科院分区：
文献类型：
--
作者：
Ebrahimi, Amir;Luo, Suhuai;Chiong, Raymond
背景：阿尔茨海默病（AD）是发达国家最致命的疾病之一。早期AD检测后的治疗可以显着延迟机构化并延长患者的独立性。人们越来越关注使用人工智能进行早期AD检测。卷积神经网络（CNN）已被证明是基于图像的应用的革命性技术，并已被应用于大脑扫描。近年来，研究在磁共振成像（MRI）扫描中使用二维（2D）CNN进行AD检测。为了在三维（3D）MRI体积上应用2D CNN，每个MRI扫描都被分成2D图像切片。通过计算每个受试者的标签和每个图像切片的预测输出之间的损失函数，在图像切片上训练CNN。虽然2D CNN可以发现图像切片中的空间依赖性，但它们无法理解3D MRI体积中2D图像切片之间的时间依赖性。本研究旨在通过对CNN产生的MRI特征序列进行建模来解决这一问题，CNN具有用于AD检测的深度序列网络。方法：本文中使用的CNN是在ImageNet数据集上进行ResNet-18预训练的。所采用的基于序列的模型是时间卷积网络（TCN）和不同类型的递归神经网络。结果：我们提出的TCN模型取得了最好的分类性能，准确率为91.78%，灵敏度为91.56%，特异度为92%。结论：我们的研究结果表明，应用基于序列的模型可以将2D和3D CNN用于AD检测的分类准确率提高10%。
Background: Alzheimer's disease (AD) is one of the deadliest diseases in developed countries. Treatments following early AD detection can significantly delay institutionalisation and extend patients' independence. There has been a growing focus on early AD detection using artificial intelligence. Convolutional neural networks (CNNs) have proven revolutionary for image-based applications and have been applied to brain scans. In recent years, studies have utilised two-dimensional (2D) CNNs on magnetic resonance imaging (MRI) scans for AD detection. To apply a 2D CNN on three-dimensional (3D) MRI volumes, each MRI scan is split into 2D image slices. A CNN is trained over the image slices by calculating a loss function between each subject's label and each image slice's predicted output. Although 2D CNNs can discover spatial dependencies in an image slice, they cannot understand the temporal dependencies among 2D image slices in a 3D MRI volume. This study aims to resolve this issue by modelling the sequence of MRI features produced by a CNN with deep sequence-based networks for AD detection.Method: The CNN utilised in this paper was ResNet-18 pre-trained on an ImageNet dataset. The employed sequence-based models were the temporal convolutional network (TCN) and different types of recurrent neural networks. Several deep sequence-based models and configurations were implemented and compared for AD detection.Results: Our proposed TCN model achieved the best classification performance with 91.78% accuracy, 91.56% sensitivity and 92% specificity.Conclusion: Our results show that applying sequence-based models can improve the classification accuracy of 2D and 3D CNNs for AD detection by up to 10%.