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 and Christoffer Nellåker ()
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Claudia Vanea: University of Oxford
Jelisaveta Džigurski: University of Tartu
Valentina Rukins: University of Tartu
Omri Dodi: Hadassah Hebrew University Medical Center
Siim Siigur: Tartu University Hospital
Liis Salumäe: Tartu University Hospital
Karen Meir: Hadassah Hebrew University Medical Center
W. Tony Parks: University of Toronto
Drorith Hochner-Celnikier: Hadassah Hebrew University Medical Center
Abigail Fraser: University of Bristol
Hagit Hochner: Hebrew University of Jerusalem
Triin Laisk: University of Tartu
Linda M. Ernst: NorthShore University HealthSystem
Cecilia M. Lindgren: University of Oxford
Christoffer Nellåker: University of Oxford

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-46986-2

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