BMNet: A New Region-Based Metric Learning Method for Early Alzheimer’s Disease Identification With FDG-PET Images

作者：Wenju Cui, Caiying Yan, Zhuangzhi Yan, Yunsong Peng, Yilin Leng, Chenlu Liu, Shuangqing Chen, Xi Jiang, Jian Zheng, Xiaodong Yang · 发表于：Frontiers in Neuroscience · 年份：2022 · DOI：10.3389/fnins.2022.831533 · 被引用次数：29 · 研究领域：Dementia and Cognitive Impairment Research、Brain Tumor Detection and Classification、Neurological Disease Mechanisms and Treatments

18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) reveals altered brain metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Some biomarkers derived from FDG-PET by computer-aided-diagnosis (CAD) technologies have been proved that they can accurately diagnosis normal control (NC), MCI, and AD. However, existing FDG-PET-based researches are still insufficient for the identification of early MCI (EMCI) and late MCI (LMCI). Compared with methods based other modalities, current methods with FDG-PET are also inadequate in using the inter-region-based features for the diagnosis of early AD. Moreover, considering the variability in different individuals, some hard samples which are very similar with both two classes limit the classification performance. To tackle these problems, in this paper, we propose a novel bilinear pooling and metric learning network (BMNet), which can extract the inter-region representation features and distinguish hard samples by constructing the embedding space. To validate the proposed method, we collect 898 FDG-PET images from Alzheimer's disease neuroimaging initiative (ADNI) including 263 normal control (NC) patients, 290 EMCI patients, 147 LMCI patients, and 198 AD patients. Following the common preprocessing steps, 90 features are extracted from each FDG-PET image according to the automatic anatomical landmark (AAL) template and then sent into the proposed network. Extensive fivefold cross-validati...