Application of convolutional neural network on early human embryo segmentation during in vitro fertilization

Abstract

Selection of the best quality embryo is the key for a faithful implantation in in vitro fertilization (IVF) practice. However, the process of evaluating numerous images captured by time-lapse imaging (TLI) system is time-consuming and some important features cannot be recognized by naked eyes. Convolutional neural network (CNN) is used in medical imaging yet in IVF. The study aims to apply CNN on day-one human embryo TLI. We first presented CNN algorithm for day-one human embryo segmentation on three distinct features: zona pellucida (ZP), cytoplasm and pronucleus (PN). We tested the CNN performance compared side-by-side with manual labelling by clinical embryologist, then measured the segmented day-one human embryo parameters and compared them with literature reported values. The precisions of segmentation were that cytoplasm over 97%, PN over 84% and ZP around 80%. For the morphometrics data of cytoplasm, ZP and PN, the results were comparable with those reported in literatures, which showed high reproducibility and consistency. The CNN system provides fast and stable analytical outcome to improve work efficiency in IVF setting. To conclude, our CNN system is potential to be applied in practice for day-one human embryo segmentation as a robust tool with high precision, reproducibility and speed.

Keywords: convolutional neural network; cytoplasm; day-one human embryo segmentation; pronucleus; time-lapse imaging; zona pellucida.