Cao Ping, Gao Jie, Zhang Zuping
School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Brain Sci. 2020 Mar 20;10(3):181. doi: 10.3390/brainsci10030181.
Mild cognitive impairment (MCI) is the early stage of Alzheimer's disease (AD). Automatic diagnosis of MCI by magnetic resonance imaging (MRI) images has been the focus of research in recent years. Furthermore, deep learning models based on 2D view and 3D view have been widely used in the diagnosis of MCI. The deep learning architecture can capture anatomical changes in the brain from MRI scans to extract the underlying features of brain disease. In this paper, we propose a multi-view based multi-model (MVMM) learning framework, which effectively combines the local information of 2D images with the global information of 3D images. First, we select some 2D slices from MRI images and extract the features representing 2D local information. Then, we combine them with the features representing 3D global information learned from 3D images to train the MVMM learning framework. We evaluate our model on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our proposed model can effectively recognize MCI through MRI images (accuracy of 87.50% for MCI/HC and accuracy of 83.18% for MCI/AD).
轻度认知障碍（MCI）是阿尔茨海默病（AD）的早期阶段。通过磁共振成像（MRI）图像自动诊断MCI一直是近年来的研究热点。此外，基于二维视图和三维视图的深度学习模型已广泛应用于MCI的诊断。深度学习架构可以从MRI扫描中捕捉大脑的解剖变化，以提取脑部疾病的潜在特征。在本文中，我们提出了一种基于多视图的多模型（MVMM）学习框架，该框架有效地将二维图像的局部信息与三维图像的全局信息相结合。首先，我们从MRI图像中选择一些二维切片，并提取代表二维局部信息的特征。然后，我们将它们与从三维图像中学到的代表三维全局信息的特征相结合，以训练MVMM学习框架。我们在阿尔茨海默病神经影像倡议（ADNI）数据库上评估我们的模型。实验结果表明，我们提出的模型可以通过MRI图像有效地识别MCI（MCI/HC的准确率为87.50%，MCI/AD的准确率为83.18%）。
