A Scalable High Throughput Fully Automated Pipeline for the Quantification of Amyloid Pathology in Alzheimer’s Disease using Deep Learning Algorithms

Abstract

The most common approach to characterize neuropathology in Alzheimer's disease (AD) involves a manual survey and inspection by an expert neuropathologist of postmortem tissue that has been immunolabeled to visualize the presence of amyloid beta in plaques and around blood vessels and neurofibrillary tangles of the tau protein. In the case of amyloid beta pathology, a semiquantitative score is given that is based on areas of densest pathology. The approach has been well-validated but the process is laborious and time-consuming, and inherently susceptible to intra- and inter-observer variability. Moreover, the tremendous growth in genetic, transcriptomic, and proteomic data from AD patients has created new opportunities to link clinical features of AD to molecular pathogenesis through pathology, but the lack of high throughput quantitative and comprehensive approaches to assess neuropathology limits the associations that can be discovered. To address these limitations, we designed a computational pipeline to analyze postmortem tissue from AD patients in a fully automated, unbiased, and high throughput manner. We used deep learning to train algorithms with a Mask Regional-Convolutional Neural Network to detect and classify different types of amyloid pathology with human-level accuracy. After training on pathology slides from the Mt Sinai cohort, our algorithms identified amyloid pathology in samples made at an independent brain bank and from an unrelated cohort of patients, indicating that the algorithms were detecting reproducible and generalizable pathology features. We designed the pipeline to retain the position of the pathology it detects, making it possible to reconstruct a map of pathology across the entire whole slide image, facilitating neuropathological analyses at multiple scales. Quantitative measurements of amyloid pathology correlated positively and significantly with the severity of AD as measured by standard approaches. We conclude that we have developed a computational pipeline to analyze digitized images of neuropathology in high throughput and algorithms to detect types of amyloid pathology with human-level accuracy that should enable a neuropathological analysis of large tissue collections and integration of those results with orthogonal clinical and multi-omic measurements.

Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations

“…Although substantial morphological differences are well-documented between pretangles and mature tangles, NFTs may have undiscovered morphological nuances, particularly those characterized by distinct fibril structures, which are only unveiled through cryo-EM imaging [1,52]. Our study releases open-source NFT segmentation models trained solely from point annotations, requiring less annotator investment, delivering comparable object detection performance to published models, and exhibiting strong correlation with expert-assigned whole-slide image semi-quantitative grades [6,8,10,53,54].…”

“…Convolutional Neural Networks (CNNs) and their variants have demonstrated remarkable capabilities for image recognition and segmentation [2] tasks in the medical domain [3]. In neuropathology, deep learning on digitized whole slide images (WSIs) of brain tissue can automate detecting and quantifying distinct pathological features such as amyloid beta plaques [4][5][6]. This includes recognizing and quantifying the NFTs central to AD diagnosis and staging [7][8][9][10].…”