| Start Date: | 5/23/2014 | Start Time: | 8:00 AM |
| End Date: | 5/23/2014 | End Time: | 9:30 AM |
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Event Description
Title: Robust Automatic Multi-Sperm Tracking in Time-Lapse Images
Advisor: Dr. Moshe Kam
Date: Friday, May 23, 2014
Time: 8:00 a.m.
Location: MEM Seminar Room, Room 162, 1st Floor, Curtis Hall
Abstract
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, we present a robust automatic multi-sperm tracking algorithm that can measure dynamic sperm motility parameters over time in pre-recorded time-lapse images. This effort is 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, we demonstrate that their methodologies can be used for sperm tracking and motility analysis. To resolve sperm measurement-to-track association conflicts, we 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). We validated the accuracy of our tracking and motility analysis by using simulated sperm trajectories whose ground truth tracks were perfectly known. Using real sperm samples collected from five patients at a fertility clinic, we demonstrated automatic multi-sperm detection, tracking and motility parameter measurement even during challenging multi-sperm collision events.
Combined analysis, simulation and testing support the use of probabilistic data association techniques to achieve robust automatic multi-sperm tracking. This method could provide fertility specialists with new data visualizations and interpretations previously impossible with existing laboratory protocols. |
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Location:
MEM Seminar Room, Room 162, 1st Floor, Curtis Hall |
Audience:
Current StudentsFacultyStaffGraduate Students |
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