Automated cell stage predictions in early mouse and human embryos using convolutional neural networks

During in-vitro fertilization, the timings of cell divisions in early human embryos are important predictors of embryo viability. Recent developments in time-lapse microscopy (TLM) allows for observing cell divisions in much greater detail than before. However, it is a time-consuming process relying on highly trained staff and subjective observations. We present an automated method based on a convolutional neural network to predict cell divisions from original (unprocessed) TLM images. Our method was evaluated on two embryo TLM image datasets: a public dataset with mouse embryos and a private dataset with human embryos up to 4-cell stage. Compared to embryologists' annotations, our results were almost 100% accurate for mouse embryos and accurate within five frames in 93% of cell stage transitions for human embryos. Our approach can be used to improve consistency and quality of existing annotations or as part of a platform for fully automatic embryo assessment.