Prioritization of Informative Regions in PET Scans for Classification of Alzheimer's Disease

Abstract: Alzheimer’s Disease (AD) is a widespread neurodegenerative disease. The disease causes brain atrophy, resulting in memory loss, decreased cognitive ability, and eventually death. There is currently no cure for the disease, but treatment may delay the onset. Therefore, it is crucial to detect the disease at an early stage. Medical imaging techniques, such as Positron Emission Tomography (PET), are heavily applied for this task. In recent years, machine learning approaches have shown success in identifying AD from such images. The thesis presents a pipeline approach to detect, extract and evaluate Region of Interest (ROI) for prioritization of informative regions in PET scans for classification of Alzheimer’s disease. The pipeline applies data acquired from Alzheimer’s Disease Neuroimaging Initiative (ADNI). An analysis of Weakly-Supervised Object Localization (WSOL) is discussed for detection of informative regions particularly indicative of AD. WSOL analyse the original full-volume 18F-fluorodeoxyglucose (18F-FDG)-PET scan to categorize the informative regions on subjects into Cognitively Normal (CN), Mild Cognitive Impairment (MCI), or AD. The detection of informative regions are processed to two approaches to extract ROI on the full-volume 18F-FDG-PET scan: Bounding-Box (BBox) Generatio nand Automated Anatomical Labeling (AAL) Generation. BBoxes Generation restricts the 18F-FDG-PET scans for Convolutional Neural Network (CNN) to BBox proposal swith particularly informative regions. The second approach ranks the anatomical regions of the brain through brain parcellation with the pre-defined atlas AAL3, and restricts a CNN to the highest-ranked regions. The results evaluate if ROIs increase the robustness for classification in relationto full-volume 18F-FDG-PET scan. The results suggest that full-volume 18F-FDG-PET with heavily restricted image size does not decrease classification performance. Instead, the BBox Generation results in a significant classification performance improvement on the test set from an Area under the ROC Curve (AuC) score of 70.08% to 97.73% and accuracy from 51.79% to 88.03%. AAL Generation suggests that the middle and inferior regions of the temporal lobe and the fusiform are essential to the classification. In addition, several regions of the frontal lobe were found to be highly important but could not alone discriminate between CN, MCI, and AD.