Deep Learning Models for Multi-Part Morphological Segmentation and Evaluation of Live Unstained Human Sperm.

To perform accurate computer vision quality assessments of sperm used within reproductive medicine, a clear separation of each sperm component from the background is critical. This study systematically evaluates and compares the performance of Mask R-CNN, YOLOv8, YOLO11, and U-Net in multi-part sperm segmentation, focusing on the head, acrosome, nucleus, neck, and tail. This study conducts a quantitative analysis using a dataset of live, unstained human sperm, employing multiple metrics, including IoU, Dice, Precision, Recall, and F1 Score. The results indicate that Mask R-CNN outperforms other models in segmenting smaller and more regular structures (head, nucleus, and acrosome). In particular, it achieves a slightly higher IoU than YOLOv8 for the nucleus and surpasses YOLO11 for the acrosome, highlighting its robustness. For the neck, YOLOv8 performs comparably to or slightly better than Mask R-CNN, suggesting that single-stage models can rival two-stage models under certain conditions. For the morphologically complex tail, U-Net achieves the highest IoU, demonstrating the advantage of global perception and multi-scale feature extraction. These findings provide insights into model selection for sperm segmentation tasks, facilitating the optimization of segmentation architectures and advancing applications in assisted reproduction and biological image analysis.