Male infertility is mostly related to semen and spermatozoa, and any diagnosis or treatment requires the investigation of the motility patterns of spermatozoa. The movements of spermatozoa are fast and involve collision and occlusion with each other. In order to extract the motility patterns of spermatozoa, multi-target tracking (MTT) of spermatozoa is necessary. One of the most important steps of MTT is data association, in which the newly arrived observations are used to update the previous tracks. Dynamic Bayesian network (DBN) is a powerful tool for modeling and solving various types of problems such as tracking and classification. There can also be a hybrid-DBN (HDBN), in which both continuous and discrete nodes are present. HDBN has a suitable structure for modeling problems that have both discrete and continuous parameters like MTT. In this research, the data association for MTT of human spermatozoa has been studied. The proposed algorithm was tested over hundreds of manually extracted spermatozoa tracks and evaluated using several standard measures. The superior results of the proposed algorithm in comparison to the other well-known algorithms, show that it could be considered as an improved alternative to traditional computer assisted sperm analysis (CASA) algorithms.

中文翻译：

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使用混合动态贝叶斯网络在相差显微镜图像序列中对人类精子的多目标跟踪。

男性不育主要与精液和精子有关，任何诊断或治疗都需要研究精子的运动方式。精子的运动较快，相互之间有碰撞和闭塞。为了提取精子的运动模式，精子的多目标跟踪（MTT）是必要的。MTT最重要的步骤之一是数据关联，其中新到达的观测值用于更新以前的轨道。动态贝叶斯网络（DBN）是用于建模和解决各种类型的问题（例如跟踪和分类）的强大工具。也可以有一个混合DBN（HDBN），其中同时存在连续节点和离散节点。HDBN具有用于建模具有离散参数和连续参数（如MTT）的问题的合适结构。在这项研究中，已经研究了人类精子MTT的数据关联。拟议的算法在数百个手动提取的精子轨道上进行了测试，并使用几种标准方法进行了评估。与其他众所周知的算法相比，所提出算法的优越结果表明，它可以被认为是传统计算机辅助精子分析（CASA）算法的改进替代方案。