Machine Learning Based Tool for Automated Sperm Cell Tracking and Sperm Bundle Detection
Autoři
Rok
2024
Publikováno
Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. Cham: Springer, 2024. p. 19-32. Lecture Notes in Computer Science. vol. 14950. ISSN 2945-9133. ISBN 978-3-031-70380-5.
Typ
Stať ve sborníku
Pracoviště
Anotace
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.