Detecting Anatomical Landmarks for Fast Alzheimer's Disease Diagnosis.
Detecting Anatomical Landmarks for Fast Alzheimer's Disease Diagnosis.
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DOI：
10.1109/tmi.2016.2582386
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发表时间：
2016-12
影响因子：
10.6
通讯作者：
Shen D
中科院分区：
文献类型：
--
作者：
Zhang J;Gao Y;Gao Y;Munsell BC;Shen D
结构磁共振成像（MRI）是一种非常流行和有效的技术，用于诊断阿尔茨海默病（AD）。使用结构MRI数据的计算机辅助诊断方法的成功在很大程度上取决于两个耗时的步骤：1）跨受试者的非线性配准，以及2）脑组织分割。为了克服这一局限性，我们提出了一种基于地标的特征提取方法，不需要非线性配准和组织分割。在训练阶段，为了区分AD受试者与健康对照（HC），首先进行基于局部形态特征的组比较，以识别具有显著组差异的脑区域。通常，所识别的区域的中心成为能够将AD受试者与HC区分开的界标位置（或简称为AD界标）。在测试阶段，使用学习的AD地标，使用基于形状约束回归森林算法的有效技术在测试图像中检测相应的地标。为了提高检测精度，还识别了一组额外的显著且一致的地标以指导AD地标检测。基于所识别的AD标志，提取形态特征以训练能够预测AD状况的支持向量机（SVM）分类器。在实验中，我们的方法进行了评估地标检测和AD分类顺序。具体而言，建议的地标检测器的地标检测误差（手动注释与自动检测）为2.41毫米，我们的基于地标的AD分类准确率为83.7%。最后，我们的方法的AD分类性能与现有的基于区域和基于体素的方法相当，甚至更好，而所提出的方法大约快50倍。
Structural magnetic resonance imaging (MRI) is a very popular and effective technique used to diagnose Alzheimer’s disease (AD). The success of computer-aided diagnosis methods using structural MRI data is largely dependent on the two time-consuming steps: 1) nonlinear registration across subjects, and 2) brain tissue segmentation. To overcome this limitation, we propose a landmark-based feature extraction method that does not require nonlinear registration and tissue segmentation. In the training stage, in order to distinguish AD subjects from healthy controls (HCs), group comparisons, based on local morphological features, are first performed to identify brain regions that have significant group differences. In general, the centers of the identified regions become landmark locations (or AD landmarks for short) capable of differentiating AD subjects from HCs. In the testing stage, using the learned AD landmarks, the corresponding landmarks are detected in a testing image using an efficient technique based on a shape-constrained regression-forest algorithm. To improve detection accuracy, an additional set of salient and consistent landmarks are also identified to guide the AD landmark detection. Based on the identified AD landmarks, morphological features are extracted to train a support vector machine (SVM) classifier that is capable of predicting the AD condition. In the experiments, our method is evaluated on landmark detection and AD classification sequentially. Specifically, the landmark detection error (manually annotated versus automatically detected) of the proposed landmark detector is 2.41mm, and our landmark-based AD classification accuracy is 83.7%. Lastly, the AD classification performance of our method is comparable to, or even better than, that achieved by existing region-based and voxel-based methods, while the proposed method is approximately 50 times faster.