Comparison of U-Net Based Models for Human Embryo Segmentation


Uysal N., Yozgatlı K., Yıldızcan E. N. , Kar E., Gezer M., Baştu E.

Bilişim Teknolojileri Dergisi, vol.1, no.1, pp.1-11, 2022 (Peer-Reviewed Journal)

  • Publication Type: Article / Article
  • Volume: 1 Issue: 1
  • Publication Date: 2022
  • Journal Name: Bilişim Teknolojileri Dergisi
  • Journal Indexes: Applied Science & Technology Source, Computer & Applied Sciences, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.1-11
  • Istanbul University Affiliated: Yes

Abstract

The quality of human embryos produced during in vitro fertilization is conventionally graded by clinical

embryologists and this process is time-consuming and prone to human error. Artificial intelligence methods may be

used to grade images captured by time-lapse microscopy (TLM). Segmentation of embryos from the background of

TLM images is an essential step for embryo quality assessment as the background of the embryo has various artifacts

which may mislead the grading algorithms. In this study, we performed a comparative analysis of automated day-5

human embryo (blastocyst) image segmentation methods based on deep learning. Four fully convolutional deep models,

including U-Net and its three variants, were created using the combination of two gradient descent-based optimizers and

two-loss functions and compared to our proposed model. The experimental results on the test set confirmed that our

customized Dilated Inception U-Net model with Adam optimizer and Dice loss outperformed other U-Net variants with

Dice coefficient, Jaccard index, accuracy, and precision of 98.68%, 97.52%, 99.20%, and 98.52%, respectively.