Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations.

ABSTRACT: Accumulation of abnormal tau protein into neurofibrillary tangles (NFTs) is a pathologic hallmark of Alzheimer disease (AD). Accurate detection of NFTs in tissue samples can reveal relationships with clinical, demographic, and genetic features through deep phenotyping. However, expert manual analysis is time-consuming, subject to observer variability, and cannot handle the data amounts generated by modern imaging. We present a scalable, open-source, deep-learning approach to quantify NFT burden in digital whole slide images (WSIs) of post-mortem human brain tissue. To achieve this, we developed a method to generate detailed NFT boundaries directly from single-point-per-NFT annotations. We then trained a semantic segmentation model on 45 annotated 2400μm by 1200μm regions of interest (ROIs)

SUBMITTER: Ghandian S 

PROVIDER: S-EPMC11463656 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature