Mapping cell-to-tissue graphs across human placenta histology whole slide images using deep learning with HAPPY.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
28 Mar 2024
28 Mar 2024
Historique:
received:
22
11
2022
accepted:
15
03
2024
medline:
29
3
2024
pubmed:
29
3
2024
entrez:
29
3
2024
Statut:
epublish
Résumé
Accurate placenta pathology assessment is essential for managing maternal and newborn health, but the placenta's heterogeneity and temporal variability pose challenges for histology analysis. To address this issue, we developed the 'Histology Analysis Pipeline.PY' (HAPPY), a deep learning hierarchical method for quantifying the variability of cells and micro-anatomical tissue structures across placenta histology whole slide images. HAPPY differs from patch-based features or segmentation approaches by following an interpretable biological hierarchy, representing cells and cellular communities within tissues at a single-cell resolution across whole slide images. We present a set of quantitative metrics from healthy term placentas as a baseline for future assessments of placenta health and we show how these metrics deviate in placentas with clinically significant placental infarction. HAPPY's cell and tissue predictions closely replicate those from independent clinical experts and placental biology literature.
Identifiants
pubmed: 38548713
doi: 10.1038/s41467-024-46986-2
pii: 10.1038/s41467-024-46986-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2710Subventions
Organisme : Bill & Melinda Gates Foundation
ID : INV-024200
Pays : United States
Informations de copyright
© 2024. The Author(s).
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