Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3317-3321. doi: 10.1109/EMBC46164.2021.9629923.
Alzheimer's disease (AD) is a neurodegenerative disease leading to irreversible and progressive brain damage. Close monitoring is essential for slowing down the progression of AD. Magnetic Resonance Imaging (MRI) has been widely used for AD diagnosis and disease monitoring. Previous studies usually focused on extracting features from whole image or specific slices separately, but ignore the characteristics of each slice from multiple perspectives and the complementarity between features at different scales. In this study, we proposed a novel classification method based on the fusion of multi-view 2D and 3D convolutions for MRI-based AD diagnosis. Specifically, we first use multiple sub-networks to extract the local slice-level feature of each slice in different dimensions. Then a 3D convolution network was used to extract the global subject-level information of MRI. Finally, local and global information were fused to acquire more discriminative features. Experiments conducted on the ADNI-1 and ADNI-2 dataset demonstrated the superiority of this proposed model over other state-of-the-art methods for their ability to discriminate AD and Normal Controls (NC). Our model achieves 90.2% and 85.2% of accuracy on ADNI-2 and ADNI-1 respectively, thus it can be effective in AD diagnosis. The source code of our model is freely available at https://github.com/fengduqianhe/ADMultiView.
阿尔茨海默病（AD）是一种神经退行性疾病，可导致不可逆转和进行性的大脑损伤。密切监测对于减缓 AD 的进展至关重要。磁共振成像（MRI）已广泛用于 AD 的诊断和疾病监测。以前的研究通常侧重于从整个图像或特定切片中分别提取特征，但忽略了从多个角度对每个切片的特征以及不同尺度特征之间的互补性。在这项研究中，我们提出了一种基于多视角 2D 和 3D 卷积融合的新分类方法，用于基于 MRI 的 AD 诊断。具体来说，我们首先使用多个子网络从不同维度提取每个切片的局部切片级特征。然后使用 3D 卷积网络提取 MRI 的全局主体级信息。最后，融合局部和全局信息以获取更具判别力的特征。在 ADNI-1 和 ADNI-2 数据集上进行的实验表明，与其他最先进的方法相比，该模型在区分 AD 和正常对照组（NC）方面具有优越性。我们的模型在 ADNI-2 和 ADNI-1 上的准确率分别达到 90.2%和 85.2%，因此可有效用于 AD 诊断。我们模型的源代码可在 https://github.com/fengduqianhe/ADMultiView 上免费获取。
