摘要
目的 构建一个基于卷积神经网络的深度学习分类模型，以鉴别阿尔茨海默病（Alzheimer＇s disease，AD）和行为异常型额颞叶痴呆（behavioral variant of frontotemporal dementia，bvFTD）患者，旨提高鉴别诊断正确率。方法分别对医生诊断的47例很可能AD患者和39例很可能bvFTD患者进行头颅MRI扫描，对结构MRI数据进行预处理后，根据AAL2模板和Harvard-Oxford模板提取全脑不同脑区的灰质体积特征，采用卷积神经网络进行构建分类模型，并对构建的模型进行与传统机器学习方法的对比试验、主要差异脑区的分类试验和年龄差异探讨试验。结果卷积神经网络基于AAL2模板和Harvard-Oxford模板数据的最高分类正确率分别为82.6%和83.7%，明显优于传统机器学习方法的75.6%和76.7%，2个模板的26个和31个可能主要差异脑区的最高分类正确率分别为79.1%和80.2%。结论AD与bvFTD可以通过基于卷积神经网络的深度学习模型获得较高的分类正确率，提示该模型可用于辅助鉴别诊断。
Objective A convolutional neural network based deep learning model was constructed to distinguish between Alzheimer disease （AD） and behavioral variant frontotemporal dementia （bvFTD） in individual patients, and to improve the accuracy of their differential diagnosis.MethodsHigh-resolution 3D structural brain images of 47 probable AD and 39 probable bvFTD were acquired by a 3.0 Tesla MRI scanner. According to the AAL2 template and Harvard-Oxford template, the ROI （region of interest） brain gray matter volume features were extracted from processed structural MRI data. A convolutional neural network based classification model was constructed, and its performance was compared to that of traditional classification algorithms in machine learning. Experiments on main different brain regions of AD and bvFTD were implemented, and the influence of subjects＇ ages on the proposed model was also explored.ResultsUsing AAL2 template and Harvard-Oxford template, the highest classification accuracies of the proposed convolutional neural network model were 82.6% and 83.7%, respectively. They were higher than classification accuracies of traditional machine learning algorithms, 75.6% and 76.7%. Using data of the most different ROIs between AD and bvFTD （26 ROIs in AAL2 template and 31 ROIs in Harvard-Oxford template）, the highest classification accuracies generated by the proposed model were 79.1%和80.2%.ConclusionThe computational model based on convolutional neural network can be used to distinguish disease-specific gray matter patterns in patients with AD and those with bvFTD, and can help the clinician to arrive at a better differential diagnosis of AD and bvFTD.
作者
杨剑
刘宁
熊凌川
王晓
孙志羽
王志江
王华丽
于欣
Yang Jian;Liu Ning;Xiong Lingchuan;Wang Xiao;Sun Zhiyu;Wang Zhijiang;Wang Huali;Yu Xin(Faculty of Information Technology,Beijing University of Technology,Beifing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics,Beijing International Collaboration Base on Brain Informatics and Wisdom Services,Beijing 100124,China)
出处
《中华精神科杂志》
CAS
CSCD
北大核心
2018年第4期228-234,共7页
Chinese Journal of Psychiatry
基金
国家重点研发计划（2017YFC1311100）
北京市科技计划课题（Z161100002616021）
国家自然科学基金（81701777,81171018）
