Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran.
Department of Andrology, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran.
Sci Rep. 2018 Mar 22;8(1):5068. doi: 10.1038/s41598-018-23435-x.
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.
男性不育主要与精液和精子有关，任何诊断或治疗都需要调查精子的运动模式。精子的运动速度很快，涉及到彼此之间的碰撞和阻塞。为了提取精子的运动模式，需要对精子进行多目标跟踪（MTT）。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迹。动态贝叶斯网络（DBN）是一种用于建模和解决各种问题的强大工具，如跟踪和分类。也可以使用混合 DBN（HDBN），其中同时存在连续和离散节点。HDBN 具有适合于具有离散和连续参数的问题建模的结构，如 MTT。在这项研究中，研究了人类精子 MTT 的的数据关联。所提出的算法已经在数百个手动提取的精子轨迹上进行了测试，并使用了几种标准度量进行了评估。与其他著名算法相比，所提出算法的优越结果表明，它可以被视为对传统计算机辅助精子分析（CASA）算法的改进替代方案。
