  We present the first deep learning model for the analysis of intracytoplasmic
sperm injection (ICSI) procedures. Using a dataset of ICSI procedure videos, we
train a deep neural network to segment key objects in the videos achieving a
mean IoU of 0.962, and to localize the needle tip achieving a mean pixel error
of 3.793 pixels at 14 FPS on a single GPU. We further analyze the variation
between the dataset's human annotators and find the model's performance to be
comparable to human experts.
