Sperm Head

Research on sperm head morphology focuses on developing automated, accurate, and non-destructive methods for analyzing sperm quality, crucial for diagnosing male infertility and improving assisted reproductive technologies (ART). Current research employs advanced image analysis techniques, including convolutional neural networks (CNNs), instance-aware segmentation, and deep reinforcement learning, to achieve precise sperm head segmentation, morphology classification, and motility tracking from microscopic images and videos. These advancements aim to reduce reliance on time-consuming manual analysis, improve diagnostic consistency, and ultimately enhance the success rates of ART procedures. The development of robust and efficient algorithms for sperm head analysis holds significant promise for improving male fertility diagnosis and treatment.