Manual semen evaluation methods are subjective and time-consuming. In this study, a deep learning algorithmic framework was designed to enable non-invasive multidimensional morphological analysis of live sperm in motion, improve current clinical sperm morphology testing methods, and significantly contribute to the advancement of assisted reproductive technologies. We improved the FairMOT tracking algorithm by incorporating the distance and angle of the same sperm head movement in adjacent frames, as well as the head target detection frame IOU value, into the cost function of the Hungarian matching algorithm. For sperm morphology, we used the BlendMask segmentation method to segment individual sperm. SegNet was used to separate the head, midpiece, and principal piece comments from each sperm. Experienced in vivo sperm physicians confirmed a morphological accuracy percentage of 90.82%. A total of 1272 samples were collected from multiple tertiary hospitals for validation of the system, which were also evaluated by physicians. The results of our system were highly consistent with those of manual microscopy. This study realized the automated detection of progressive motility and morphology of sperm simultaneously, which is crucial for selection of morphologically normal and motile sperm for intracytoplasmic sperm injection.

中文翻译：

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基于多目标跟踪的活体精子多维形态分析

手动精液评估方法主观且耗时。本研究设计了深度学习算法框架，能够对运动中的活精子进行无创多维形态学分析，改进当前临床精子形态检测方法，为辅助生殖技术的进步做出重大贡献。我们改进了FairMOT跟踪算法，将相邻帧中相同精子头部运动的距离和角度，以及头部目标检测帧IOU值纳入匈牙利匹配算法的成本函数中。对于精子形态，我们使用 BlendMask 分割方法来分割单个精子。SegNet 用于将每个精子的头部、中段和主段注释分开。经验丰富的活体精子医师证实形态准确率为90.82%。系统验证共从多家三级医院采集了1272份样本，并由医生进行了评估。我们的系统的结果与手动显微镜的结果高度一致。该研究实现了精子渐进运动和形态的同时自动化检测，这对于选择形态正常且运动的精子进行胞浆内单精子注射至关重要。