A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view. To obtain correct results, manual evaluation requires extensive training. Therefore, computer-assisted sperm analysis (CASA) has become increasingly used in clinics. Despite this, more data is needed to train supervised machine learning approaches in order to improve accuracy and reliability in the assessment of sperm motility and kinematics. In this regard, we provide a dataset called VISEM-Tracking with 20 video recordings of 30 seconds (comprising 29,196 frames) of wet sperm preparations with manually annotated bounding-box coordinates and a set of sperm characteristics analyzed by experts in the domain. In addition to the annotated data, we provide unlabeled video clips for easy-to-use access and analysis of the data via methods such as self- or unsupervised learning. As part of this paper, we present baseline sperm detection performances using the YOLOv5 deep learning (DL) model trained on the VISEM-Tracking dataset. As a result, we show that the dataset can be used to train complex DL models to analyze spermatozoa.

翻译：手动评估精子活力需要显微镜观察，但由于视野中快速运动的精子，这一过程极具挑战。为获得正确结果，手动评估需要大量培训。因此，计算机辅助精子分析（CASA）在临床中的应用日益广泛。尽管如此，仍需要更多数据来训练监督式机器学习方法，以提高精子活力和运动学评估的准确性与可靠性。为此，我们提供了名为VISEM-Tracking的数据集，包含20段时长30秒（共29,196帧）的精液湿片视频记录，并附有手动标注的边界框坐标以及由领域专家分析的一组精子特征。除标注数据外，我们还提供了未标记的视频片段，便于通过自监督或无监督学习等方法轻松访问和分析数据。作为本文的一部分，我们展示了使用基于VISEM-Tracking数据集训练的YOLOv5深度学习（DL）模型进行精子检测的基准性能。结果表明，该数据集可用于训练复杂的深度学习模型以分析精子。