Combined segmentation and classification of Alzheimer’s disease pathology distribution within human hippocampal subregions

Alzheimer’s disease (AD) is a complex neurodegenerative disease characterized by progressive amyloid and tau pathology. Quantifying and classifying AD pathology across the human brain is challenging due to its large size and expertise needed for critical evaluation of the morphological variety in structure types (e.g. diffuse vs dense plaques, neurofibrillary tangles, neuropil threads, etc.). High‐throughput microscopy imaging and artificial intelligence allow for large‐scale, quantitative ‘digital pathology’ surveys. Due to their structural differences, distinct pathological aggregates in microscopy images can be identified and quantified by trained artificial neural networks (Tang et al 2019). However, current approaches do not incorporate anatomical segmentation to map the distribution of AD pathology across whole tissue sections. Understanding where and when AD pathology is distributed across different brain regions is critical to defining cell type susceptibility to neurodegeneration. This project’s goal was to create a combined neural network classification and segmentation quantification approach to generate anatomically‐accurate pathology maps of the human hippocampus and investigate the spatial distribution of distinct types of pathology.