Hi‐LabSpermTracking: A Novel and High‐Quality Sperm Tracking Dataset with an Advanced Ensemble Detection and Tracking Approach for Real‐World Clinical Scenarios

OpenAlex İngilizce

Sperm motility, a critical factor in diagnosing male infertility, requires computer‐based solutions due to the limitations of manual evaluation methods. This study introduces the Hi‐LabSpermTracking dataset, comprising 66 videos (60 s each, 10 fps) collected from 14 patients and meticulously annotated by experts. Unlike similar datasets, these uninterrupted, long‐duration videos enable continuous tracking of individual sperm cells, each assigned a unique ID throughout the video, supporting both sperm detection and tracking tasks. Experimental evaluations employ you only look once v8 (YOLOv8), real‐time detection transformer, and simple online and realtime tracking with a deep association metric across three scenarios. In Scenario I (sperm detection), the YOLOv8n model achieves 98.9% mAP50 and 97.9% F1‐score. In Scenario II (sperm tracking), performance metrics include 83.88% mAP50, 87.63% F1‐score, 72.27% higher order tracking accuracy (HOTA), and 77.88% multiple object tracking accuracy (MOTA). Scenario III simulates real‐world challenges by separating training and testing videos. Ensemble methods are applied, with the proposed mean ensemble achieving superior results: 86.55% mAP50, 87.87% F1‐score, 66.66% HOTA, and 76.42% MOTA. The Hi‐LabSpermTracking dataset enables robust sperm tracking research, while the mean ensemble method amplifies accuracy by uniting model strengths.