Research on classification method of multimodal magnetic resonance images of Alzheimer's disease based on generalized convolutional neural networks

被引：0

作者：

Qin Z. ^{[1
,2
]}

Liu Z. ^{[3
]}

Lu Y. ^{[4
]}

Zhu P. ^{[1
,2
]}

机构：

[1] School of Mechanical Engineering, Shanghai Jiao Tong University, 200240, Shanghai

[2] National Engineering Research Center of Automotive Power and Intelligent Control, Shanghai Jiao Tong University, 200240, Shanghai

[3] School of Design, Shanghai Jiao Tong University, 200240, Shanghai

[4] Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 200240, Shanghai

来源：

Shengwu Yixue Gongchengxue Zazhi/Journal of Biomedical Engineering | 2023年 / 40卷 / 02期

关键词：

Alzheimer’s disease; Generalized convolutional neural networks; Graph convolutional networks; Hybrid attention mechanism; Multimodal magnetic resonance images;

D O I：

10.7507/1001-5515.202212046

中图分类号：

学科分类号：

摘要：

阿尔茨海默病（AD）是一种进行性、不可逆的神经系统退行性疾病，基于磁共振成像（MRI）的神经影像学检查是进行AD筛查与诊断最直观、可靠的方法之一。临床上头颅MRI检测会产生多模态影像数据，为解决多模态MRI处理与信息融合的问题，本文提出基于广义卷积神经网络（gCNN）的结构MRI和功能MRI特征提取与融合方法。该方法针对结构MRI提出基于混合注意力机制的三维残差U型网络（3D HA-ResUNet）进行特征表示与分类；针对功能MRI提出U型图卷积神经网络（U-GCN）进行脑功能网络的节点特征表示与分类。在两类影像特征融合的基础上，基于离散二进制粒子群优化算法筛选最优特征子集，并使用机器学习分类器输出预测结果。来自AD神经影像学计划（ADNI）开源数据库的多模态数据集验证结果表明，本文所提出的模型在各自数据域内都有优秀的表现，而gCNN框架结合了两类模型的优势，进一步提高使用单一模态MRI的方法性能，将分类准确率和敏感性分别提升了5.56%和11.11%。综上，本文所提出的基于gCNN的多模态MRI分类方法可以为AD的辅助诊断提供技术基础。.; Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. Neuroimaging based on magnetic resonance imaging (MRI) is one of the most intuitive and reliable methods to perform AD screening and diagnosis. Clinical head MRI detection generates multimodal image data, and to solve the problem of multimodal MRI processing and information fusion, this paper proposes a structural and functional MRI feature extraction and fusion method based on generalized convolutional neural networks (gCNN). The method includes a three-dimensional residual U-shaped network based on hybrid attention mechanism (3D HA-ResUNet) for feature representation and classification for structural MRI, and a U-shaped graph convolutional neural network (U-GCN) for node feature representation and classification of brain functional networks for functional MRI. Based on the fusion of the two types of image features, the optimal feature subset is selected based on discrete binary particle swarm optimization, and the prediction results are output by a machine learning classifier. The validation results of multimodal dataset from the AD Neuroimaging Initiative (ADNI) open-source database show that the proposed models have superior performance in their respective data domains. The gCNN framework combines the advantages of these two models and further improves the performance of the methods using single-modal MRI, improving the classification accuracy and sensitivity by 5.56% and 11.11%, respectively. In conclusion, the gCNN-based multimodal MRI classification method proposed in this paper can provide a technical basis for the auxiliary diagnosis of Alzheimer's disease.

引用

页码：217 / 225

页数：8