Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK.
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Nat Commun. 2024 Mar 28;15(1):2710. doi: 10.1038/s41467-024-46986-2.
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.
准确的胎盘病理学评估对于母婴健康管理至关重要，但胎盘的异质性和时间变异性给组织学分析带来了挑战。为了解决这个问题，我们开发了“Histology Analysis Pipeline.PY”（HAPPY），这是一种用于量化胎盘组织学全切片图像中细胞和微观解剖组织结构变异性的深度学习层次方法。HAPPY 与基于斑块的特征或分割方法不同，它遵循可解释的生物学层次结构，以单细胞分辨率代表组织内的细胞和细胞群落，跨越整个幻灯片图像。我们提出了一组来自健康足月胎盘的定量指标，作为未来评估胎盘健康的基线，并展示了这些指标在具有临床意义的胎盘梗死的胎盘中的变化情况。HAPPY 的细胞和组织预测与独立临床专家和胎盘生物学文献的预测非常吻合。
