Comparison of U-Net Based Models for Human Embryo Segmentation

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.