Embryo Graphs: Predicting Human Embryo Viability from 3D Morphology

Embryo selection is a critical step in the process of in-vitro fertilisation in which embryologists choose the most viable embryos for transfer into the uterus. In recent years, numerous works have used computer vision to perform embryo selection. However, many of these works have neglected the fact that the embryo is a 3D structure, instead opting to analyse embryo images captured at a single focal plane. In this paper we present a method for the 3D reconstruction of cleavage-stage human embryos. Through a user study, we validate that our reconstructions align with expert assessments. Furthermore, we demonstrate the utility of our approach by generating graph representations that capture biologically relevant features of the embryos. In pilot experiments, we train a graph neural network on these representations and show that it outperforms existing methods in predicting live birth from euploid embryo transfers. Our findings suggest that incorporating 3D reconstruction and graph-based analysis can improve automated embryo selection.