Machine Learning Based Tool for Automated Sperm Cell Tracking and Sperm Bundle Detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F24%3A00377076" target="_blank" >RIV/68407700:21240/24:00377076 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-031-70381-2_2" target="_blank" >https://doi.org/10.1007/978-3-031-70381-2_2</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-70381-2_2" target="_blank" >10.1007/978-3-031-70381-2_2</a>
Alternative languages
Result language
angličtina
Original language name
Machine Learning Based Tool for Automated Sperm Cell Tracking and Sperm Bundle Detection
Original language description
This study introduces a novel machine learning-based methodology for automated detection and tracking of sperm cells within microscopic video recordings, aiming to elucidate the dynamics and motion patterns of individual sperm cells as well as sperm cell bundles. At first, the method identifies sperm cells across successive frames within a video sequence, facilitating the reconstruction of each cell's trajectory over time. Subsequently, we introduce a classification algorithm that distinguishes between solitary sperm cells, clusters of adjacent cells, and cohesive sperm cell bundles, addressing a gap in existing methodologies. Finally, we employ three conventional metrics for velocity assessment: Straight Line Velocity (VSL) and Average Path Velocity (VAP) and Curvilinear velocity (VCL), to quantify the movement speed of both individual sperm cells and bundles. The approach represents a significant advancement in the automated analysis of sperm motility and aggregation phenomena, providing a robust tool for researchers to study sperm behavior with enhanced accuracy and efficiency. The integration of machine learning techniques in sperm cell detection and tracking offers promising insights into reproductive biology and fertility studies.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track
ISBN
978-3-031-70380-5
ISSN
2945-9133
e-ISSN
1611-3349
Number of pages
14
Pages from-to
19-32
Publisher name
Springer
Place of publication
Cham
Event location
Vilnius
Event date
Sep 9, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001330399500002