基于阿尔茨海默病诊断的全连接多核卷积神经网络

Fully Connected Multi-Kernel Convolutional Neural Network Based on Alzheimer's Disease Diagnosis.

作者信息

Deng Lan, Wang Yuanjun

机构信息

School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, P.R. China.

出版信息

J Alzheimers Dis. 2023;92(1):209-228. doi: 10.3233/JAD-220519.

DOI:10.3233/JAD-220519

PMID:36710670

Abstract

BACKGROUND

There is a shortage of clinicians with sufficient expertise in the diagnosis of Alzheimer's disease (AD), and cerebrospinal fluid biometric collection and positron emission tomography diagnosis are invasive. Therefore, it is of potential significance to obtain high-precision automatic diagnosis results from diffusion tensor imaging (DTI) through deep learning, and simultaneously output feature probability maps to provide clinical auxiliary diagnosis.

OBJECTIVE

We proposed a factorization machine combined neural network (FMCNN) model combining a multi-function convolutional neural network (MCNN) with a fully convolutional network (FCN), while accurately diagnosing AD and mild cognitive impairment (MCI); corresponding fiber bundle visualization results are generated to describe their status.

METHODS

First, the DTI data is preprocessed to eliminate the influence of external factors. The fiber bundles of the corpus callosum (CC), cingulum (CG), uncinate fasciculus (UNC), and white matter (WM) were then tracked based on deterministic fiber tracking. Then the streamlines are input into CNN, MCNN, and FMCNN as one-dimensional features for classification, and the models are evaluated by performance evaluation indicators. Finally, the fiber risk probability map is output through FMCNN.

RESULTS

After comparing the model performance indicators of CNN, MCNN, and FMCNN, it was found that FMCNN showed the best performance in the indicators of accuracy, specificity, sensitivity, and area under the curve. By inputting the fiber bundles of the 10 regions of interest (UNC_L, UNC_R, UNC, CC, CG, CG+UNC, CG+CC, CC+UNC, CG+CC+UNC, and WM into CNN, MCNN, and FMCNN, respectively), WM shows the highest accuracy in CNN, MCNN, and FMCNN, which are 88.41%, 92.07%, and 96.95%, respectively.

CONCLUSION

The FMCNN proposed here can accurately diagnose AD and MCI, and the generated fiber probability map can represent the risk status of AD and MCI.

摘要

背景

在阿尔茨海默病（AD）诊断方面具备足够专业知识的临床医生短缺，且脑脊液生物标志物采集和正电子发射断层扫描诊断具有侵入性。因此，通过深度学习从扩散张量成像（DTI）中获得高精度自动诊断结果，并同时输出特征概率图以提供临床辅助诊断具有潜在意义。

目的

我们提出了一种因子分解机结合神经网络（FMCNN）模型，该模型将多功能卷积神经网络（MCNN）与全卷积网络（FCN）相结合，同时准确诊断AD和轻度认知障碍（MCI）；生成相应的纤维束可视化结果以描述其状态。

方法

首先，对DTI数据进行预处理以消除外部因素的影响。然后基于确定性纤维追踪对胼胝体（CC）、扣带束（CG）、钩束（UNC）和白质（WM）的纤维束进行追踪。接着将流线作为一维特征输入到卷积神经网络（CNN）、MCNN和FMCNN中进行分类，并通过性能评估指标对模型进行评估。最后，通过FMCNN输出纤维风险概率图。

结果

比较CNN、MCNN和FMCNN的模型性能指标后发现，FMCNN在准确率、特异性、敏感性和曲线下面积指标方面表现最佳。通过将10个感兴趣区域（UNC_L、UNC_R、UNC、CC、CG、CG+UNC、CG+CC、CC+UNC、CG+CC+UNC和WM）的纤维束分别输入到CNN、MCNN和FMCNN中，WM在CNN、MCNN和FMCNN中的准确率最高，分别为88.41%、92.07%和96.95%。