İnsan Embriyo Segmentasyonu için U-Net Tabanlı Modellerin Karşılaştırılması

Abstract

The quality of human embryos produced during in vitro fertilization is conventionally graded by clinicalembryologists and this process is time-consuming and prone to human error. Artificial intelligence methods may beused to grade images captured by time-lapse microscopy (TLM). Segmentation of embryos from the background ofTLM images is an essential step for embryo quality assessment as the background of the embryo has various artifactswhich may mislead the grading algorithms. In this study, we performed a comparative analysis of automated day-5human 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 andtwo-loss functions and compared to our proposed model. The experimental results on the test set confirmed that ourcustomized Dilated Inception U-Net model with Adam optimizer and Dice loss outperformed other U-Net variants withDice coefficient, Jaccard index, accuracy, and precision of 98.68%, 97.52%, 99.20%, and 98.52%, respectively.