SVFR: A novel slice-to-volume feature representation framework using deep neural networks and a clustering model for the diagnosis of Alzheimer's disease

Rubing Wang; Linlin Gao; Xiaoling Zhang; Jinming Han; Alzheimer's Disease Neuroimaging Initiative

doi:10.1016/j.heliyon.2023.e23008

SVFR: A novel slice-to-volume feature representation framework using deep neural networks and a clustering model for the diagnosis of Alzheimer's disease

Heliyon. 2023 Dec 3;10(1):e23008. doi: 10.1016/j.heliyon.2023.e23008. eCollection 2024 Jan 15.

Authors

Rubing Wang¹, Linlin Gao^{2

1}, Xiaoling Zhang³, Jinming Han⁴; Alzheimer's Disease Neuroimaging Initiative

Affiliations

¹ Faculty of Electrical Engineering and Computer Science, Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Ningbo University, Ningbo, China.
² School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China.
³ Ningbo Medical Center Lihuili Hospital, Ningbo, China.
⁴ Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.

Abstract

Deep neural networks (DNNs) have been effective in classifying structural magnetic resonance imaging (sMRI) images for Alzheimer's disease (AD) diagnosis. In this study, we propose a novel two-phase slice-to-volume feature representation (SVFR) framework for AD diagnosis. Specifically, we design a slice-level feature extractor to automatically select informative slice images and extract their slice-level features, by combining DNN and clustering models. Furthermore, we propose a joint volume-level feature generator and classifier to hierarchically aggregate the slice-level features into volume-level features and to classify images, by devising a spatial pyramid set pooling module and a fusion module. Experimental results demonstrate the superior performance of the proposed SVFR, surpassing the majority of the state-of-the-art methods and achieving comparable results to the best-performing approach. Experimental results also showcase the efficacy of the slice-level feature extractor in the selection of informative slice images, as well as the effectiveness of the volume-level feature generator and classifier in the integration of slice-level features for image classification. The source code for this study is publicly available at https://github.com/gll89/SVFR.

Keywords: Clustering model; Deep neural networks; Diagnosis of Alzheimer's disease; Informative slice images; Slice-to-volume feature representation; Spatial pyramid set pooling module.