Synthetic Spermatozoa Video Sequences Generation Using Adversarial Imitation Learning

Abstract

Automated sperm sample analysis using computer vision techniques has gained increasing interest due to the tedious and time-consuming nature of manual evaluation. Deep learning models have been applied for sperm detection, tracking, motility analysis, and morphology recognition. However, the lack of labeled data hinders their adoption in laboratories. In this work, we propose a method to generate synthetic spermatozoa video sequences using Generative Adversarial Imitation Learning (GAIL). Our approach uses a parametric model based on Bezier splines to generate frames of a single spermatozoon. We evaluate our method against U-net and GAN-based approaches, and demonstrate its superior performance.