Image analysis for Alzheimer's disease prediction: Embracing pathological hallmarks for model architecture design

• URL: http://arxiv.org/abs/2011.06531v3
• Date: Mon, 10 May 2021 08:50:41 GMT
• Title: Image analysis for Alzheimer's disease prediction: Embracing pathological hallmarks for model architecture design
• Authors: Sarah C. Br\"uningk, Felix Hensel, Catherine R. Jutzeler, Bastian Rieck
• Abstract summary: Alzheimer's disease (AD) is associated with local (e.g. brain tissue atrophy) and global brain changes (loss of cerebral connectivity) We introduce a novel, highly-scalable approach that simultaneously captures $textitlocal$ and $textitglobal$ changes in the diseased brain. It is based on a neural network architecture that combines patch-based, high-resolution 3D-CNNs with global topological features.
• Score: 8.583436410810204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Alzheimer's disease (AD) is associated with local (e.g. brain tissue atrophy) and global brain changes (loss of cerebral connectivity), which can be detected by high-resolution structural magnetic resonance imaging. Conventionally, these changes and their relation to AD are investigated independently. Here, we introduce a novel, highly-scalable approach that simultaneously captures $\textit{local}$ and $\textit{global}$ changes in the diseased brain. It is based on a neural network architecture that combines patch-based, high-resolution 3D-CNNs with global topological features, evaluating multi-scale brain tissue connectivity. Our local-global approach reached competitive results with an average precision score of $0.95\pm0.03$ for the classification of cognitively normal subjects and AD patients (prevalence $\approx 55\%$).

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