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Article

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

1 Mar 2024

Abstract excerpt

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...

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Share to Ovarian function and disorders
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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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PMID: 39803335
DOI: 10.1016/j.csbj.2024.02.025

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