Towards Deep Cellular Phenotyping in Placental Histology

Michael Ferlaino, Craig A. Glastonbury, Carolina Motta-Mejia, Manu Vatish, Ingrid Granne, Stephen Kennedy, Cecilia M. Lindgren, Christoffer Nellaker

Abstract: The placenta is a complex organ, playing multiple roles during fetal development. Very little is known about the association between placental morphological abnormalities and fetal physiology. In this work, we present an open sourced, computationally tractable deep learning pipeline to analyse placenta histology at the level of the cell. By utilising two deep Convolutional Neural Network architectures and transfer learning, we can robustly localise and classify placental cells within five classes with an accuracy of 89%. Furthermore, we learn deep embeddings encoding phenotypic knowledge that is capable of both stratifying five distinct cell populations and learn intraclass phenotypic variance. We envisage that the automation of this pipeline to population scale studies of placenta histology has the potential to improve our understanding of basic cellular placental biology and its variations, particularly its role in predicting adverse birth outcomes.

Author Affiliation: University of Oxford

Keywords: Placental Histology, Deep Learning, CNN, Digital Pathology, Medical Imaging, Computer Vision

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/towards-deep-cellular-phenotyping-in/code)