全脑解剖学标志稀疏关联的阿尔茨海默症诊断  

Diagnosis of Alzheimer′s Disease with Sparse Association of Whole Brain Anatomical Markers

作　　者：郑菲 唐奇伶[1] 刘汝璇 张美玲 葛威 Zheng Fei;Tang Qiling;Liu Ruxuan;Zhang Meiling;Ge Wei(School of Biomedical Engineering,South-Central Minzu University,Wuhan 430074,China)

机构地区：[1]中南民族大学生物医学工程学院,武汉430074

出　　处：《中国生物医学工程学报》2022年第6期708-716,共9页Chinese Journal of Biomedical Engineering

基　　金：中南民族大学中央高校基本科研业务费专项资金(CZY22014)。

摘　　要：阿尔茨海默症(AD)是一种复杂的神经退行性疾病,伴有记忆和其他精神功能的进行性损害,是造成老年人死亡的主要原因,如何对AD进行准确的诊断至关重要。根据生物学验证,已知人脑中的多个脑区在解剖学上和功能上是相互联系的,但现有的研究方法往往忽略了潜在的特征间的关系。因此,关注大脑不同区域的特征相关性有利于提高脑认知疾病的检测性能。本研究提出一种以数据驱动的方式自动识别全脑结构磁共振成像(sMRI)中解剖学标志点,并基于解剖点提取块特征,采用全局关联将各个块的特征进行深度融合,通过计算块与块之间的相互作用,实现大脑各个脑区的相互关联。其次,根据块之间的关联度差异,进行阈值化处理,利用稀疏关联模块去除冗余信息,进一步提高特征的判别能力,最后,利用稀疏后的深层特征构建分类模型,对AD患者个体进行预测。使用包含198例AD患者和224例健康人的ADNI-1数据集进行训练,包含152例AD患者和196例健康人的ADNI-2数据集进行测试,结果表明,该方法的准确率和灵敏度分别达到0.9368和0.9211,所提出的方法更多地考虑了块与块之间的联系以及关联度差异,可望更有效地对AD进行诊断。Alzheimer′s disease(AD)is a complex neurodegenerative disease with progressive impairment of memory and other mental functions,which is the main cause of death in the elderly.How to make an accurate diagnosis of AD is crucial.Existing research methods ignore the relationship between potential features.However,according to biological verification,it is known that many brain regions in the human brain are interconnected anatomically and functionally.Therefore,focusing on the characteristic correlation of different brain regions is beneficial to improve the detection performance of brain cognitive diseases.In this paper,we propose a data-driven method for automatic recognition of anatomical markers in whole brain structural magnetic resonance imaging(sMRI),extract block features based on anatomical points,deeply fuse the features of each block using global correlation,and realize the correlation of various brain regions by calculating the interaction between blocks.Secondly,according to the difference of correlation degree between blocks,thresholding processing is carried out,and redundant information is removed by sparse correlation module to further improve the distinguishing ability of features.Finally,classification model is constructed by using the deep features after sparse to predict Alzheimer′s disease individuals.The experimental results showed that the accuracy and sensitivity of the method reached 0.9368 and 0.9211,respectively,when the ADNI-1 dataset containing 198 AD patients and 224 healthy subjects were trained,and the ADNI-2 dataset containing 152 AD and 196 healthy subjects were tested.The proposed method takes into account the relationship between blocks and the difference of correlation degree,and can diagnose Alzheimer′s disease more effectively.

关 键 词：阿尔茨海默症诊断 解剖学标志点 全局关联 交互映射 稀疏 

分 类 号：R318[医药卫生—生物医学工程]