Automated cell division classification in early mouse and human embryos using convolutional neural networks

During in vitro fertilization (IVF), the timing of cell divisions in early human embryos is a key predictor of embryo viability. Recent developments in time-lapse microscopy (TLM) have allowed us to observe cell divisions in much greater detail than previously possible. However, it is a time-consuming process that relies on a highly trained staff and subjective observations. We describe an automated method based on a convolutional neural network to detect and classify cell divisions from original (unprocessed) TLM images. Here, we used two embryo TLM image datasets to evaluate our method: a public dataset with mouse embryos up to the 4-cell stage and a private dataset with human embryos up to the 8-cell stage. Compared to embryologists' annotations, our results were almost 100% accurate for the mouse embryo images and accurate within five frames in 93.9% of cell stage transitions for the human embryos. Our approach can be used to improve the consistency and quality of the existing annotations or as part of a platform for fully automated embryo assessment. The code is available at http://github.com/JonasEMalmsten/CellDivision..