HAPPY: A deep learning pipeline for mapping cell-to-tissue graphs across placenta histology whole slide images

The placenta is a heterogeneous and rapidly evolving organ. Digital pathology presents a novel set of approaches to histology analysis but has yet to be applied for the placenta. We present the 'Histology Analysis Pipeline.PY' (HAPPY), a hierarchical method for quantifying the variability of cells and micro-anatomical tissue structures across placenta histology whole slide images. In contrast to commonly used high-level patch features or segmentation approaches, HAPPY follows an interpretable biological hierarchy. It is an end-to-end pipeline able to represent cells and communities of cells within tissues at a single-cell resolution across whole slide images. Here we present a set of quantitative metrics from healthy term placentas as a baseline for future assessments of placenta health. HAPPY cell and tissue predictions closely replicate those from independent clinical experts and the placenta biology literature. ### Competing Interest Statement The authors have declared no competing interest.