3D convolutional neural networks for Alzheimer’s disease classification

Dementia of the Alzheimer’s type (DAT) is a neurodegenerative disease characterized by abnormal brain metabolism and structural brain atrophy. These functional and structural changes can be observed in images acquired using 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) and structural magnetic resonance imaging (MRI). Traditional machine learning framework for DAT classification often involves time-consuming segmentation of brain images as part of the feature extraction process, while deep neural networks can learn DAT-related patterns directly from brain images to generate DAT probability scores. In this thesis, we design 3D convolutional neural networks (CNN) for two applications: classification and segmentation. We design classification networks for single modality use and perform comprehensive evaluation by measuring the performance of our networks on images along the entire DAT spectrum. To support traditional DAT classification framework, we design a fast and accurate segmentation pipeline. We propose a hemisphere-based approach where we train networks to localize and segment hemispheres.