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Urbano, Leonardo F.. (Drexel University) “Robust Automatic Multi-Sperm Tracking in Time-Lapse Images”, Advisor: Kam, Moshe (2014)
Human sperm cell counting, tracking and motility analysis is of significant interest to biologists studying sperm function and to medical practitioners evaluating male infertility. Today, the prevailing method for analyzing sperm at fertility clinics and research laboratories is laborious and subjective. Namely, the number and quality of sperm are often visually appraised by technicians using a microscope. Although total sperm count and sperm concentration can be reasonably estimated when standard protocols are applied, they have little diagnostic value except in identifying pathologically extreme abnormalities. More dynamic sperm swimming parameters such as curvilinear velocity (VCL), straight-line velocity (VSL), linearity of forward progression (LIN) and amplitude of lateral head displacement (ALH) are increasingly believed to have clinical significance in predicting infertility but are impossible for a human observer to visually discern. Expensive computer-assisted semen analysis (CASA) instruments are also sometimes used but are severely encumbered by crude ad-hoc tracking algorithms which cannot track sperm in close proximity or whose paths intersect and are typically limited to analyzing video clips of < 1 sec duration.
In this thesis, the author presented a robust automatic multi-sperm tracking algorithm that can measure dynamic sperm motility parameters over time in pre-recorded time-lapse images. This effort was informed by progress in signal processing and target tracking technologies over the last three decades. Multi-target tracking algorithms originally developed for radar, sonar and video processing have addressed similar problems in other domains. In this thesis, the author demonstrated that the proposed methodologies can be used for sperm tracking and motility analysis. To resolve sperm measurement-to-track association conflicts, the author applied and evaluated three multi-target tracking algorithms: the probabilistic data association filter (PDAF), the joint probabilistic data association filter (JPDAF) and the exact nearest neighbor extension to the JPDAF (ENN-JPDAF). The author validated the accuracy of our tracking and motility analysis by using simulated sperm trajectories whose ground truth tracks were perfectly known. Using samples collected from five patients at a fertility clinic, the author demonstrated automatic sperm detection and tracking even during challenging multi-sperm collision events.
Combined analysis, testing and simulation support the use of probabilistic data association techniques robust automatic multi-sperm tracking. This method could provide fertility specialists with new data visualizations and interpretations previously impossible with existing laboratory protocols.
For details, please visit the thesis page.
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