  A major challenge in clinical In-Vitro Fertilization (IVF) is selecting the
highest quality embryo to transfer to the patient in the hopes of achieving a
pregnancy. Time-lapse microscopy provides clinicians with a wealth of
information for selecting embryos. However, the resulting movies of embryos are
currently analyzed manually, which is time consuming and subjective. Here, we
automate feature extraction of time-lapse microscopy of human embryos with a
machine-learning pipeline of five convolutional neural networks (CNNs). Our
pipeline consists of (1) semantic segmentation of the regions of the embryo,
(2) regression predictions of fragment severity, (3) classification of the
developmental stage, and object instance segmentation of (4) cells and (5)
pronuclei. Our approach greatly speeds up the measurement of quantitative,
biologically relevant features that may aid in embryo selection.
