Machine Learning Based Tool for Automated Sperm Cell Tracking and Sperm Bundle Detection

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine Learning Based Tool for Automated Sperm Cell Tracking and Sperm Bundle Detection

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Machine Learning Based Tool for Automated Sperm Cell Tracking and Sperm Bundle Detection

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track

ISBN

978-3-031-70380-5

ISSN

2945-9133

e-ISSN

1611-3349

Počet stran výsledku

14

Strana od-do

19-32

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Vilnius

Datum konání akce

9. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001330399500002