Graph of Hippocampal Subfields Grading for Alzheimer’s Disease Prediction
Numerous methods have been proposed to capture early hippocampus alterations caused by Alzheimer’s disease. Among them, patch-based grading approach showed its capability to capture subtle structural alterations. This framework applied on hippocampus obtains state-of-the-art results for AD detection but is limited for its prediction compared to the same approaches based on whole-brain analysis. We assume that this limitation could come from the fact that hippocampus is a complex structure divided into different subfields. Indeed, it has been shown that AD does not equally impact hippocampal subfields. In this work, we propose a graph-based representation of the hippocampal subfields alterations based on patch-based grading feature. The strength of this approach comes from better modeling of the inter-related alterations through the different hippocampal subfields. Thus, we show that our novel method obtains similar results than state-of-the-art approaches based on whole-brain analysis with improving by 4 percent points of accuracy patch-based grading methods based on hippocampus.
KeywordsHippocampal subfields Patch-based grading Graph-based method Alzheimer’s disease classification Mild Cognitive Impairment
This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) within the project DeepVolbrain and in the frame of the investments for the future program IdEx Bordeaux (HL-MRI ANR-10-IDEX-03-02), Cluster of excellence CPU, TRAIL (BigDataBrain ANR-10-LABX- 57) and the Spanish DPI2017-87743-R grant from the Ministerio de Economia, Industria y Competitividad of Spain.
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