卷积神经网络诊断阿尔兹海默症的方法

doi:10.11772/j.issn.1001-9081.2017.12.3504

计算机应用 ›› 2017, Vol. 37 ›› Issue (12): 3504-3508.DOI: 10.11772/j.issn.1001-9081.2017.12.3504

卷积神经网络诊断阿尔兹海默症的方法

林伟铭^1,2,3, 高钦泉^1,2, 杜民^1,4

1. 福州大学物理与信息工程学院, 福州 350108;
2. 福建省医疗器械与医药技术重点实验室, 福州 350108;
3. 厦门理工学院光电与通信工程学院, 福建厦门 361024;
4. 福建省生态产业绿色技术重点实验室, 福建南平 354300

收稿日期:2017-06-29 修回日期:2017-09-06 出版日期:2017-12-10 发布日期:2017-12-18
通讯作者: 杜民
作者简介:林伟铭(1983-),男,福建漳州人,讲师,博士研究生,主要研究方向:机器学习、深度学习、医学图像处理;高钦泉(1986-),男,福建福清人,副教授,博士,主要研究方向:机器学习、医学图像处理;杜民(1955-)女,福建惠安人,教授,博士生导师,博士,主要研究方向:医学图像处理。
基金资助:
福建省自然科学基金资助项目（2016J05157）；福建省中青年教师教育科研项目（JAT160074）；厦门市科技计划项目（3502Z20153017）。

Convolutional neural network based method for diagnosis of Alzheimer's disease

LIN Weiming^1,2,3, GAO Qinquan^1,2, DU Min^1,4

1. College of Physics and Information Engineering, Fuzhou University, Fuzhou Fujian 350108, China;
2. Fujian Provincial Key Laboratory of Medical Instrument and Pharmaceutical Technology, Fuzhou Fujian 350108, China;
3. School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen Fujian 361024, China;
4. Fujian Provincial Key Laboratory of Eco-Industrial Green Technology, Nanping Fujian 354300, China

Received:2017-06-29 Revised:2017-09-06 Online:2017-12-10 Published:2017-12-18
Supported by:
This work is partially supported by the Natural Science Foundation of Fujian Province (2016J05157), the Foundation of Educational and Scientific Research Project for Young and Middle-aged Teachers of Fujian Province (JAT160074), the Xiamen Municipal Science and Technology Project (3502Z20153017).

摘要/Abstract

摘要： 针对阿尔兹海默症（AD）通常会导致海马体区域萎缩的现象，提出一种使用卷积神经网络（CNN）对脑部磁共振成像（MRI）的海马体区域进行AD识别的方法。测试数据来自ADNI数据库提供的188位患者和229位正常人的脑部MRI图像。首先，将所有脑图像进行颅骨剥离，并配准到标准模板；其次，使用线性回归进行脑部萎缩的年龄矫正；然后，经过预处理后，从每个对象的3D脑图像的海马体区域提取出多幅2.5D的图像；最后，使用CNN对这些图像进行训练和识别，将同一个对象的图像识别结果用于对该对象的联合诊断。通过多次十折交叉验证方式进行实验，实验结果表明所提方法的平均识别准确率达到88.02%。与堆叠自动编码器（SAE）方法进行比较，比较结果表明，所提方法在仅使用MRI进行诊断的情况下效果比SAE方法有较大提高。

关键词: 阿尔兹海默症, 卷积神经网络, 磁共振成像, 海马体, 计算机辅助诊断

Abstract: The Alzheimer's Disease (AD) usually leads to atrophy of hippocampus region. According to the characteristic, a Convolutional Neural Network (CNN) based method was proposed for the diagnosis of AD by using the hippocampu region in brain Magnetic Resonance Imaging (MRI). All the test data were got from the ADNI database including 188 AD and 229 Normal Control (NC). Firstly, all the brain MRI were preprocessed by skull stripping and aligned to a template space. Secondly, a linear regression model was used for age correction of brain aging atrophy. Then, after preprocessing, multiple 2.5D images were extracted from the hippocampus region in the 3D brain image for each object. Finally, the CNN was used to train and recognize the extracted 2.5D images, and the recognition results of the same object were used for the joint diagnosis of AD. The experiments were carried out by using multiple ten-fold cross validation methods. The experimental results show that the average recognition accuracy of the proposed method reaches 88.02%. The comparison results show that, compared with Stacked Auto-Encoder (SAE) method, the proposed method has improved the diagnosis effect of AD in the case of only using MRI.

Key words: Alzheimer's Disease (AD), Convolutional Neural Network (CNN), Magnetic Resonance Imaging (MRI), Hippocampus, Computer-Aid Diagnosis (CAD)

中图分类号:

TP391.4

林伟铭, 高钦泉, 杜民. 卷积神经网络诊断阿尔兹海默症的方法[J]. 计算机应用, 2017, 37(12): 3504-3508.

LIN Weiming, GAO Qinquan, DU Min. Convolutional neural network based method for diagnosis of Alzheimer's disease[J]. Journal of Computer Applications, 2017, 37(12): 3504-3508.

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卷积神经网络诊断阿尔兹海默症的方法

Convolutional neural network based method for diagnosis of Alzheimer's disease

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