Open AccessApplication of convolutional neural network on early human embryo segmentation during in vitro fertilization
Author(s) -
Zhao Mingpeng,
Xu Murong,
Li Hanhui,
Alqawasmeh Odai,
Chung Jacqueline Pui Wah,
Li Tin Chiu,
Lee TinLap,
Tang Patrick MingKuen,
Chan David Yiu Leung
Publication year - 2021
Publication title -
journal of cellular and molecular medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.44
H-Index - 130
eISSN - 1582-4934
pISSN - 1582-1838
DOI - 10.1111/jcmm.16288
Subject(s) - convolutional neural network , pronucleus , zona pellucida , segmentation , embryo , in vitro fertilisation , artificial intelligence , computer science , pattern recognition (psychology) , embryo quality , human fertilization , biology , anatomy , embryogenesis , microbiology and biotechnology , oocyte , zygote
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.