A Dataset for Deep Learning based Cleavage-stage Blastocyst Prediction with Time-lapse Images

Recent advances in deep learning and artificial intelligence techniques have obtained notable progress in automated embryo image analysis. However, most current research focuses on blastocyst-stage embryo evaluation (more than 5 days after in vitro fertilization), which may reduce the number of transferable embryos and increase the risk of canceled circles. Therefore, this paper aims to investigate the possibility of evaluating blastocyst development at the cleavage stage with deep neural networks (DNNs). To this end, we collect a dataset that consists of time-lapse images of more than 500 embryos (about 194k frames in total). We evaluate several widely used DNNs on the dataset, including those of single-frame architectures and multi-frame architectures. Experimental results show that the accuracy of different DNNs varies from 66.42% to 77.74% and we also provide the possible reasons behind the performance gap. Our dataset and code will be published soon to facilitate related research.