Evaluating Object Detection Algorithms for Crowded Sperm Microscopy Videos

Abstract

Tracking sperm cells in crowded microscopy videos is a critical yet challenging task in reproductive biology due to high cell density, occlusions, nonlinear motion, and imaging artifacts. This study systematically evaluates the performance of three object detection algorithms— TrackPy, OpenCV, and StarDist—using unlabeled and labeled metrics. Sperm attributes were extracted from high-density phase-contrast microscopy videos using these algorithms, and unlabeled metrics (average number of sperms per frame and average frames per sperm) were computed. The algorithm outputs were also benchmarked against hand labeled ground truth data using evaluation (labeled) metrics - DET, TRA, TF, MOTA, HOTA, and IDF1. TrackPy consistently outperformed the other methods across all metrics, demonstrating robust detection and reliable temporal tracking. The findings underscore the importance of selecting appropriate algorithms for dense biological data and support the use of physics-based tracking approaches in clinical and research applications. Future work will explore algorithm adaptation and broader validation using public datasets.