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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 2400\u00a0\u03bcm by 1200\u00a0\u03bcm regions of interest (ROIs) selected from 15 unique temporal cortex WSIs of AD cases from three institutions (University of California (UC)-Davis, UC-San Diego, and Columbia University). Segmenting NFTs at the single-pixel level, the model achieved an area under the receiver operating characteristic of 0.832 and an F1 of 0.527 (196-fold over random) on a held-out test set of 664 NFTs from 20 ROIs (7 WSIs). We compared this to deep object detection, which achieved comparable but coarser-grained performance that was 60% faster. The segmentation and object detection models correlated well with expert semi-quantitative scores at the whole-slide level (Spearman\u2019s rho \u03c1\u2009=\u20090.654 (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20096.50e-5) and \u03c1\u2009=\u20090.513 (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20093.18e-3), respectively). We openly release this multi-institution deep-learning pipeline to provide detailed NFT spatial distribution and morphology analysis capability at a scale otherwise infeasible by manual assessment.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-61605-4","type":"journal-article","created":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T10:25:47Z","timestamp":1787912747000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations"],"prefix":"10.1038","volume":"16","author":[{"given":"Sina","family":"Ghandian","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liane","family":"Albarghouthi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kiana","family":"Nava","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shivam R. 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