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...
Main Authors: | , , , , , , , , , , , , , , , , , , , |
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Format: | Article |
Language: | English |
Published: |
Elsevier
2024-12-01
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Series: | Computational and Structural Biotechnology Journal |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037024000497 |