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

Jakub Horenin; Veronika Magdanz; Islam S.M. Khalil; Anke Klingner; Alexander Kovalenko; Miroslav Čepek

doi:10.1007/978-3-031-70381-2_2

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

Jakub Horenin, Veronika Magdanz, Islam S.M. Khalil, Anke Klingner, Alexander Kovalenko, Miroslav Čepek^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

7 Downloads (Pure)

Abstract

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. https://gitlab.fit.cvut.cz/horenjak/sperm_cell_tracking_apphttps://apps.datalab.fit.cvut.cz/sperm_tracking/

Original language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track
Subtitle of host publication	European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part X
Editors	Albert Bifet, Tomas Krilavičius, Ioanna Miliou, Slawomir Nowaczyk
Publisher	Springer
Pages	19-32
Number of pages	14
ISBN (Electronic)	978-3-031-70381-2
ISBN (Print)	978-3-031-70380-5
DOIs	https://doi.org/10.1007/978-3-031-70381-2_2
Publication status	Published - 2024
Event	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2024: PHD forum - Vilnius, Lithuania Duration: 9 Sept 2024 → 13 Sept 2024

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	14950
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2024
Abbreviated title	ECML PKDD 2024
Country/Territory	Lithuania
City	Vilnius
Period	9/09/24 → 13/09/24

Keywords

Bundle detection
Bundle formation
Kalman filter
Motion dynamics
Sperm cell tracking
2024 OA procedure

Access to Document

10.1007/978-3-031-70381-2_2

ArticleFinal published version, 1.15 MBLicence: Taverne

Cite this

Horenin, J., Magdanz, V., Khalil, I. S. M., Klingner, A., Kovalenko, A., & Čepek, M. (2024). Machine Learning Based Tool for Automated Sperm Cell Tracking and Sperm Bundle Detection. In A. Bifet, T. Krilavičius, I. Miliou, & S. Nowaczyk (Eds.), Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track: European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part X (pp. 19-32). (Lecture Notes in Computer Science; Vol. 14950). Springer. https://doi.org/10.1007/978-3-031-70381-2_2

Horenin, Jakub ; Magdanz, Veronika ; Khalil, Islam S.M. et al. / Machine Learning Based Tool for Automated Sperm Cell Tracking and Sperm Bundle Detection. Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track: European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part X. editor / Albert Bifet ; Tomas Krilavičius ; Ioanna Miliou ; Slawomir Nowaczyk. Springer, 2024. pp. 19-32 (Lecture Notes in Computer Science).

@inproceedings{9b3073a055e64813ad91022e27963dd7,

title = "Machine Learning Based Tool for Automated Sperm Cell Tracking and Sperm Bundle Detection",

abstract = "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{\textquoteright}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. https://gitlab.fit.cvut.cz/horenjak/sperm_cell_tracking_apphttps://apps.datalab.fit.cvut.cz/sperm_tracking/",

keywords = "Bundle detection, Bundle formation, Kalman filter, Motion dynamics, Sperm cell tracking, 2024 OA procedure",

author = "Jakub Horenin and Veronika Magdanz and Khalil, {Islam S.M.} and Anke Klingner and Alexander Kovalenko and Miroslav {\v C}epek",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2024 : PHD forum, ECML PKDD 2024 ; Conference date: 09-09-2024 Through 13-09-2024",

year = "2024",

doi = "10.1007/978-3-031-70381-2_2",

language = "English",

isbn = "978-3-031-70380-5",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "19--32",

editor = "Albert Bifet and Tomas Krilavi{\v c}ius and Ioanna Miliou and Slawomir Nowaczyk",

booktitle = "Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track",

address = "Germany",

}

Horenin, J, Magdanz, V, Khalil, ISM, Klingner, A, Kovalenko, A & Čepek, M 2024, Machine Learning Based Tool for Automated Sperm Cell Tracking and Sperm Bundle Detection. in A Bifet, T Krilavičius, I Miliou & S Nowaczyk (eds), Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track: European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part X. Lecture Notes in Computer Science, vol. 14950, Springer, pp. 19-32, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2024, Vilnius, Lithuania, 9/09/24. https://doi.org/10.1007/978-3-031-70381-2_2

Machine Learning Based Tool for Automated Sperm Cell Tracking and Sperm Bundle Detection. / Horenin, Jakub; Magdanz, Veronika; Khalil, Islam S.M. et al.
Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track: European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part X. ed. / Albert Bifet; Tomas Krilavičius; Ioanna Miliou; Slawomir Nowaczyk. Springer, 2024. p. 19-32 (Lecture Notes in Computer Science; Vol. 14950).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Machine Learning Based Tool for Automated Sperm Cell Tracking and Sperm Bundle Detection

AU - Horenin, Jakub

AU - Magdanz, Veronika

AU - Khalil, Islam S.M.

AU - Klingner, Anke

AU - Kovalenko, Alexander

AU - Čepek, Miroslav

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - 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. https://gitlab.fit.cvut.cz/horenjak/sperm_cell_tracking_apphttps://apps.datalab.fit.cvut.cz/sperm_tracking/

AB - 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. https://gitlab.fit.cvut.cz/horenjak/sperm_cell_tracking_apphttps://apps.datalab.fit.cvut.cz/sperm_tracking/

KW - Bundle detection

KW - Bundle formation

KW - Kalman filter

KW - Motion dynamics

KW - Sperm cell tracking

KW - 2024 OA procedure

UR - http://www.scopus.com/inward/record.url?scp=85203867760&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-70381-2_2

DO - 10.1007/978-3-031-70381-2_2

M3 - Conference contribution

AN - SCOPUS:85203867760

SN - 978-3-031-70380-5

T3 - Lecture Notes in Computer Science

SP - 19

EP - 32

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

A2 - Bifet, Albert

A2 - Krilavičius, Tomas

A2 - Miliou, Ioanna

A2 - Nowaczyk, Slawomir

PB - Springer

T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2024

Y2 - 9 September 2024 through 13 September 2024

ER -

Horenin J, Magdanz V, Khalil ISM, Klingner A, Kovalenko A, Čepek M. Machine Learning Based Tool for Automated Sperm Cell Tracking and Sperm Bundle Detection. In Bifet A, Krilavičius T, Miliou I, Nowaczyk S, editors, Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track: European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part X. Springer. 2024. p. 19-32. (Lecture Notes in Computer Science). doi: 10.1007/978-3-031-70381-2_2