Mapping cell-to-tissue graphs across human placenta histology whole slide images using deep learning with HAPPY

Claudia Vanea*, Jelisaveta Džigurski, Valentina Rukins, Omri Dodi, Siim Siigur, Liis Salumäe, Karen Meir, W. Tony Parks, Drorith Hochner-Celnikier, Abigail Fraser, Hagit Hochner, Triin Laisk, Linda M. Ernst, Cecilia M. Lindgren, Christoffer Nellåker*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageAmerican English
Article number2710
JournalNature Communications
Volume15
Issue number1
DOIs
StatePublished - Dec 2024

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.

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