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首页> 外文会议>Conference on Biomedical Applications in Molecular, Structural, and Functional Imaging >Automated diagnosis of Alzheimer's disease with multi-atlas based whole brain segmentations
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Automated diagnosis of Alzheimer's disease with multi-atlas based whole brain segmentations

机译：基于多标志的全脑细分的阿尔茨海默病自动诊断

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Voxel-based analysis is widely used in quantitative analysis of structural brain magnetic resonance imaging (MRI) and automated disease detection, such as Alzheimer's disease (AD). However, noise at the voxel level may cause low sensitivity to AD-induced structural abnormalities. This can be addressed with the use of a whole brain structural segmentation approach which greatly reduces the dimension of features (the number of voxels). In this paper, we propose an automatic AD diagnosis system that combines such whole brain segmentations with advanced machine learning methods. We used a multi-atlas segmentation technique to parcellate T1-weighted images into 54 distinct brain regions and extract their structural volumes to serve as the features for principal-component-analysis-based dimension reduction and support-vector-machine-based classification. The relationship between the number of retained principal components (PCs) and the diagnosis accuracy was systematically evaluated, in a leave-one-out fashion, based on 28 AD subjects and 23 age-matched healthy subjects. Our approach yielded pretty good classification results with 96.08% overall accuracy being achieved using the three foremost PCs. In addition, our approach yielded 96.43% specificity, 100% sensitivity, and 0.9891 area under the receiver operating characteristic curve.
机译：基于体素的分析广泛用于结构脑磁共振成像（MRI）和自动疾病检测的定量分析，如Alzheimer疾病（AD）。然而，体素水平的噪声可能导致对AD引起的结构异常的敏感性。这可以通过使用整个脑结构分割方法来解决，这大大减少了特征的尺寸（体素数量）。在本文中，我们提出了一种自动广告诊断系统，将这种全脑细分与先进的机器学习方法相结合。我们利用多拟标菌分段技术将T1加权图像对54个不同的脑区进行加入54个不同的大脑区域，并提取它们的结构体积以用作基于主组分 - 分析的尺寸减小和基于支持 - 矢量机的分类的特征。基于28个特征和23个年龄匹配的健康受试者，以休假时尚系统地评估保留的主要成分（PCS）数量和诊断精度之间的关系。我们的方法产生了相当良好的分类结果，使用三个最重要的PC实现了96.08％的总体准确性。此外，我们的方法在接收器操作特征曲线下产生了96.43％的特异性，100％灵敏度和0.9891个区域。

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