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.
