UCLA

Alzheimer’s Disease is a progressive and fatal neurodegenerative disease which affects millions of people around the world. The pathophysiology of the disease is characterized by the accumulation of neuritic amyloid plaques and neurofibrillary tau tangles within the hippocampus and many surrounding structures. Tau tangles, in particular, are commonly used to identify the stage of disease progression. Currently, only the presence or absence of tau tangles, together with simple staging information, in specific brain regions is noted at autopsy. An improvement to this approach is to measure the distribution of tau tangles across large brain samples in order to better characterize the progression of the disease. In this work, we build a framework for comparing the ability of different machine learning models to identify the locations of tau tangles in postmortem neural microscopy images. In particular, we focus on the development of a set of software tools which transform probability heatmaps into a set of region proposals for all the tau tangles within an image. We then construct two different machine learning models and compare their performance using a precision-recall (PR) framework.