Long Xiaojing, Chen Lifang, Jiang Chunxiang, Zhang Lijuan
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
Department of Neurology, Shenzhen University 1st Affiliated Hospital, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China.
PLoS One. 2017 Mar 6;12(3):e0173372. doi: 10.1371/journal.pone.0173372. eCollection 2017.
Detecting early morphological changes in the brain and making early diagnosis are important for Alzheimer's disease (AD). High resolution magnetic resonance imaging can be used to help diagnosis and prediction of the disease. In this paper, we proposed a machine learning method to discriminate patients with AD or mild cognitive impairment (MCI) from healthy elderly and to predict the AD conversion in MCI patients by computing and analyzing the regional morphological differences of brain between groups. Distance between each pair of subjects was quantified from a symmetric diffeomorphic registration, followed by an embedding algorithm and a learning approach for classification. The proposed method obtained accuracy of 96.5% in differentiating mild AD from healthy elderly with the whole-brain gray matter or temporal lobe as region of interest (ROI), 91.74% in differentiating progressive MCI from healthy elderly and 88.99% in classifying progressive MCI versus stable MCI with amygdala or hippocampus as ROI. This deformation-based method has made full use of the pair-wise macroscopic shape difference between groups and consequently increased the power for discrimination.
检测大脑早期形态变化并进行早期诊断对阿尔茨海默病（AD）至关重要。高分辨率磁共振成像可用于辅助该疾病的诊断和预测。在本文中，我们提出了一种机器学习方法，通过计算和分析不同组之间大脑的区域形态差异，将AD或轻度认知障碍（MCI）患者与健康老年人区分开来，并预测MCI患者的AD转化情况。通过对称微分同胚配准量化每对受试者之间的距离，随后采用嵌入算法和学习方法进行分类。所提出的方法在以全脑灰质或颞叶为感兴趣区域（ROI）区分轻度AD与健康老年人时准确率达到96.5%，在区分进展性MCI与健康老年人时准确率为91.74%，在以杏仁核或海马体为ROI对进展性MCI与稳定型MCI进行分类时准确率为88.99%。这种基于变形的方法充分利用了不同组之间成对的宏观形状差异，从而提高了辨别能力。
