Alzheimer’s Disease (AD), one of the main causes of death in elderly people, is characterized by Mild Cognitive Impairment (MCI) at prodromal stage. Nevertheless, only part of MCI subjects could progress to AD. The main objective of this paper is thus to identify those who will develop a dementia of AD type among MCI patients. ¹⁸ F-FluoroDeoxyGlucose Positron Emission Tomography ( ¹⁸ F-FDG PET) serves as a neuroimaging modality for early diagnosis as it can reflect neural activity via measuring glucose uptake at resting-state. In this paper, we design a deep network on ¹⁸ F-FDG PET modality to address the problem of AD identification at early MCI stage. To this end, a Multi-view Separable Pyramid Network (MiSePyNet) is proposed, in which representations are learned from axial, coronal and sagittal views of PET scans so as to offer complementary information and then combined to make a decision jointly. Different from the widely and naturally used 3D convolution operations for 3D images, the proposed architecture is deployed with separable convolution from slice-wise to spatial-wise successively, which can retain the spatial information and reduce training parameters compared to 2D and 3D networks, respectively. Experiments on ADNI dataset show that the proposed method can yield better performance than both traditional and deep learning-based algorithms for predicting the progression of Mild Cognitive Impairment, with a classification accuracy of 83.05%.

中文翻译：

---

通过18F-FDG脑部PET成像在MCI阶段进行AD预测的多视图可分离金字塔网络。

阿尔茨海默氏病（AD）是老年人死亡的主要原因之一，其特征是在前驱阶段出现轻度认知障碍（MCI）。但是，只有部分MCI受试者可以发展为AD。因此，本文的主要目的是确定那些将在MCI患者中发展为AD型痴呆的人。¹⁸ F- 氟脱氧葡萄糖正电子发射断层显像（ ¹⁸ F-FDG PET）可作为神经影像学手段进行早期诊断，因为它可以通过测量静止状态下的葡萄糖摄取来反映神经活动。在本文中，我们设计了一个基于¹⁸ F-FDG PET模式可解决MCI早期阶段的AD识别问题。为此，提出了一种多视图可分离金字塔网络（MiSePyNet），其中从PET扫描的轴向，冠状和矢状视图中学习表示，以便提供补充信息，然后结合起来共同做出决策。与针对3D图像的广泛使用和自然使用的3D卷积操作不同，所提出的体系结构以从切片到空间的连续卷积顺序进行部署，与2D和3D网络相比，它可以保留空间信息并减少训练参数。在ADNI数据集上进行的实验表明，与传统的基于深度学习的算法相比，该方法在预测轻度认知障碍的进展方面可提供更好的性能，