Multidimensional morphological analysis of live sperm based on multiple-target tracking

Multidimensional morphological analysis of live sperm based on multiple-target tracking

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 co...

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Bibliographic Details
Main Authors: Hao Yang, Mengmeng Ma, Xiangfeng Chen, Guowu Chen, Yi Shen, Lijun Zhao, Jianfeng Wang, Feifei Yan, Difeng Huang, Huijie Gao, Hao Jiang, Yuqian Zheng, Yu Wang, Qian Xiao, Ying Chen, Jian Zhou, Jie Shi, Yi Guo, Bo Liang, Xiaoming Teng
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Computational and Structural Biotechnology Journal
Subjects:
Non-staining
Live sperm
Morphological analysis
Multiple sperm tracking
Motility
Deep learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2001037024000497
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