Location Prediction of Sperm Cells Using Long Short-Term Memory Networks

Intracytoplasmic sperm injection (ICSI) requires precise selection of a single sperm cell in a dish to be injected into an oocyte. This task is challenging due to high sperm velocity, collision with other sperm cells, and changes in the imaging focus. Herein, a new model is proposed, which is based on a multilayer long short-term memory (LSTM) network combined with linear extrapolation calculation, for predicting the future location of individual sperm cells based on their previous paths. The model is trained with a unique loss function, constructed from the predicted location and trajectory, and results in low mean location error predictions. The results are based on comparing different input sequences length, number of time frames ahead, and motility parameters of sperm cells. This model can provide faster and more accurate sperm motility predictions, better tracking, and aid automatic sperm capturing technologies.

Automated summary

This paper proposes a new model based on a multilayer long short-term memory (LSTM) network and linear extrapolation calculation to predict the future location of individual sperm cells based on their previous paths. The model is trained with a unique loss function constructed from the predicted location and trajectory, resulting in low mean location error predictions. This model can provide faster and more accurate sperm motility predictions, better tracking, and aid automatic sperm capturing technologies.