本文選題：阿爾茲海默癥 + 輕度認(rèn)知障礙��； 參考：《南方醫(yī)科大學(xué)》2017年碩士論文

【摘要】：阿爾茲海默癥(Alzheimer's Disease,AD)是一種起病隱匿且不可逆的神經(jīng)系統(tǒng)退行性疾病,其多發(fā)于65歲以上的人群,是當(dāng)今世界最為普遍的一種癡呆癥。2016年,全球癡呆患者人數(shù)已達(dá)4700萬(wàn)人,其中50%-75%為阿爾茲海默癥患者。目前,中國(guó)的阿爾茲海默癥患者人數(shù)已居世界第一,同時(shí)中國(guó)也是全球增速最快的國(guó)家之一。然而,阿爾茲海默癥的診療率卻與發(fā)病情況呈強(qiáng)烈反差,全球阿爾茲海默癥及其它類型癡呆患者中,僅有22%接受過(guò)診斷。在中國(guó)這個(gè)比例更低,有49%的病例被誤認(rèn)為是自然老化,僅21%的患者得到了規(guī)范診斷,僅19.6%接受了藥物治療。而且就目前的醫(yī)療水平,阿爾茲海默癥是一種無(wú)法治愈的疾病。因此,對(duì)阿爾茲海默癥進(jìn)行早期診斷,早期預(yù)防與干預(yù)治療是非常重要的。輕度認(rèn)知障礙(Mild Cognitive Impairment,MCI),是前人所提出的阿爾茲海默癥的一種前驅(qū)狀態(tài),是介于正常衰老與癡呆之間的一種中間狀態(tài)。輕度認(rèn)知障礙可作為阿爾茲海默癥的“預(yù)報(bào)器”,如果能及早發(fā)現(xiàn)此狀態(tài)并給予適當(dāng)?shù)母深A(yù)治療,就可以延緩阿爾茲海默癥的進(jìn)展。所以,正確診斷阿爾茲海默癥,尤其是正確診斷其早期階段的輕度認(rèn)知障礙,對(duì)阿爾茲海默癥的預(yù)防、早期發(fā)現(xiàn)與治療干預(yù)至關(guān)重要。為了識(shí)別阿爾茲海默癥與輕度認(rèn)知障礙患者,本文提出了一種基于支持張量機(jī)(STM)的分類器,以T1加權(quán)MRI腦圖像灰質(zhì)灰度為特征的診斷方法。該分類器以三維(3D)的腦灰質(zhì)圖像作為模型輸入,用STM迭代算法訓(xùn)練分類器每一模的權(quán)向量進(jìn)而進(jìn)行分類。采集了 70例AD患者,112例MCI患者(包含在隨訪中轉(zhuǎn)化為AD的,MCI-C:MCI Converters與未轉(zhuǎn)化為AD的,MCI-NC:MCI Non-converters 各 56 例),以及 70 例正常人(NC)的 T1-Weighted MRI 三維(3D)腦圖像。首先提取每個(gè)腦圖像的灰質(zhì)來(lái)構(gòu)造每個(gè)腦圖像的三階灰質(zhì)張量,張量大小為95×119×102。采用張量主成分分析法(TPCA)取得三階灰質(zhì)張量的低維的主成分張量,并以此主成分張量作為基于STM的分類器的輸入進(jìn)行分類(STM-TPCA)。張量獨(dú)立成分分析(TICA)也用來(lái)提取出三階灰質(zhì)張量的獨(dú)立成分張量以作為基于STM的分類器的輸入進(jìn)行分類(STM-TICA)。考慮到特征之間存在冗余性,因此在支持張量機(jī)迭代算法將張量特征轉(zhuǎn)化為向量特征后,遞歸特征消除法(RFE)用來(lái)做特征選擇,獲得最優(yōu)特征子集作為分類器的輸入進(jìn)行分類(STM-RFE)。最后,對(duì)四組人群進(jìn)行分類:ADNC,MCINC,ADMCI,MCI-CMCI-NC,此分類模型采用10折交叉驗(yàn)證的方法進(jìn)行訓(xùn)練測(cè)試。對(duì)于AD與NC的分類,其正確率最高可達(dá)91.19%(敏感性92.86%,特異性89.52%);對(duì)于MCI與NC的分類,其正確率最高可達(dá)83.15%(敏感性91.67%,特異性69.52%);對(duì)于AD與MCI的分類,其正確率最高可達(dá)82.23%(敏感性65.71%,特異性92.56%);對(duì)于MCI-C與MCI-NC的分類,其正確率最高可達(dá)77.08%(敏感性77.38%,特異性76.79%)。此外,本文還結(jié)合樣本的基本信息(年齡、性別、教育程度)與認(rèn)知分?jǐn)?shù)(Mini-Mental State Exam,MMSE 分?jǐn)?shù);Alzheimer's Disease Assessment Scale-cognitive subscale,ADAS-cog分?jǐn)?shù))進(jìn)行分類,結(jié)果發(fā)現(xiàn)結(jié)合基本信息與認(rèn)知分?jǐn)?shù)后分類效果能進(jìn)一步提升,且對(duì)比于Shen與Willette結(jié)合多模態(tài)數(shù)據(jù)來(lái)作為模型輸入的研究,本文方法的分類效果皆更為優(yōu)異。以上實(shí)驗(yàn)結(jié)果表明以T1加權(quán)MRI腦圖像的灰質(zhì)圖像為特征的基于STM的分類器是一種有效的阿爾茲海默癥診斷方法;并且基本信息,認(rèn)知分?jǐn)?shù)與MRI腦灰質(zhì)圖像是相容的,具有很好的互補(bǔ)作用。在實(shí)驗(yàn)的過(guò)程中,我們發(fā)現(xiàn)由于高的張量維數(shù)(95×119×102),張量獨(dú)立成分分析和遞歸特征消除法的運(yùn)行速度都比較緩慢,高維特征很大程度上提高了特征提取和特征選擇的時(shí)間。因此,我們考慮基于13個(gè)方向4種距離的灰度共生矩陣(GLCM)的紋理特征(Texture feature)張量(12×13×4)作為基于STM的分類器的輸入進(jìn)行分類(STM-Texture)。此改進(jìn)方法減少了輸入樣本張量的維數(shù),從而提升了整個(gè)分類模型的運(yùn)行速度。實(shí)驗(yàn)結(jié)果表明使用灰度共生矩陣的紋理特征張量作為基于STM的分類器的輸入的分類方法,既能保持原本分類方法的優(yōu)越性,同時(shí)也減少了運(yùn)行時(shí)間。
[Abstract]:Alzheimer's Disease (AD) is an insidious and irreversible neurodegenerative disease. It is more prevalent in people over 65 years of age, and is the most common type of dementia in the world today.2016. The number of people with dementia in the world has reached 47 million, of which 50%-75% is Alzheimer's disease. The number of people with Alzheimer's disease is the world's first, and China is one of the fastest growing countries in the world. However, the diagnosis and treatment rate of Alzheimer's disease is strongly contrasting with the incidence of Alzheimer's disease. Only 22% of all Alzheimer's and other types of dementia worldwide have been diagnosed. In China, the proportion is lower, and 49% of the cases are misrecognized. For natural aging, only 21% of the patients received standardized diagnosis, only 19.6% received medication. And at the current level of medical treatment, Alzheimer's disease is an untreatable disease. Therefore, early diagnosis of Alzheimer's disease, early prevention and intervention is very important. Mild cognitive impairment (Mild Cognitive Impair). Ment, MCI), a precursor of Alzheimer's disease proposed by predecessors, is an intermediate state between normal aging and dementia. Mild cognitive impairment can be used as a "predictor" of Alzheimer's disease. It can delay the progress of Alzheimer's disease if it can present this state of early onset and give appropriate pre treatment. Therefore, the correct diagnosis of Alzheimer's disease, especially the correct diagnosis of mild cognitive impairment at its early stage, the prevention of Alzheimer's disease, the early detection and treatment intervention are essential. In order to identify Alzheimer's disease and mild cognitive impairment, this paper proposes a classifier based on the support tensor machine (STM) and T1 weighted MRI Diagnostic methods of cerebral gray matter images gray feature. The classifier with three-dimensional (3D) brain images as the model input, using STM iterative algorithm to train the classifier for each weight vector and then classify the first mock exam. Collected 70 cases of AD patients, 112 MCI patients (included in the follow-up into AD, MCI-C:MCI, Converters with the conversion to AD, MCI- 56 cases of NC:MCI Non-converters and 70 cases of normal human (NC) T1-Weighted MRI three-dimensional (3D) brain images. First, the gray matter of each brain image is extracted to construct the three order gray matter tensor of each brain image, and the tensor is 95 * 119 x 102. to obtain the low dimensional principal component tensor of the three order gray matter tensor by tensor principal component analysis (TPCA). The principal component tensor is classified as the input of the classifier based on STM (STM-TPCA). The tensor independent component analysis (TICA) is also used to extract the independent component tensor of the three order gray matter tensor to be classified as the input of the classifier based on the STM (STM-TICA). Considering the redundancy between the features, the tensor is supported by the tensor iteration. After the algorithm transforms the tensor features into vector features, recursive feature elimination (RFE) is used to do feature selection, and the optimal subset is classified as the input of the classifier (STM-RFE). Finally, four groups of people are classified: ADNC, MCINC, ADMCI, MCI-CMCI-NC, and this classification model is trained by 90% off cross validation methods. In the classification of AD and NC, the correct rate is up to 91.19% (sensitivity 92.86%, specificity 89.52%); for the classification of MCI and NC, the correct rate is up to 83.15% (sensitivity 91.67%, specificity 69.52%); for the classification of AD and MCI, the correct rate is up to 82.23% (sensitivity 65.71%, specificity 92.56%); the classification of MCI-C and MCI-NC is correct. The highest rate was 77.08% (sensitivity 77.38%, specificity 76.79%). In addition, the basic information (age, sex, education level) and cognitive score (Mini-Mental State Exam, MMSE score, Alzheimer's Disease Assessment Scale-cognitive subscale, ADAS-cog score) were also classified, and the results were found to be combined with basic information and recognition. The classification effect can be further improved after the knowledge of the score, and compared with the combination of Shen and Willette multimodal data as model input, the results of this method are better. The experimental results show that the STM based classifier based on the gray matter image of the T1 weighted MRI brain image is an effective Alzheimer's disease diagnosis. And the basic information, the cognitive score is compatible with the MRI gray matter image, and has a good complementarity. In the course of the experiment, we found that because of the high tensor dimension (95 * 119 x 102), the running speed of the tensor independent component analysis and the recursive feature elimination method is relatively slow, and the high dimensional features greatly improve the characteristics. Therefore, we consider the texture feature (Texture feature) Zhang Liang (12 * 13 * 4) based on the grayscale symbiotic matrix (Texture feature) based on 13 directions and 4 distances (STM-Texture) as the input of the classifier based on STM (STM-Texture). This improved method reduces the dimension of the input sample and thus improves the whole classification model. The experimental results show that the texture tensor of the grayscale symbiotic matrix is used as the classification method of the classifier based on STM, which can not only maintain the superiority of the original classification method, but also reduce the running time.
【學(xué)位授予單位】：南方醫(yī)科大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R749.16;R445.2

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本文編號(hào)：1914373