Bull Sperm Tracking and Machine Learning-Based Motility Classification

Abstract

Sperm motility measurement using computer assisted sperm analysis (CASA) has been widely accepted as a substitute for manual measurement but still faces several challenges. In the tracking phase, tracking errors caused by detection failure often occur when measuring fresh bull semen. Tracking errors occur for two reasons: (1) the sperm move very fast, which makes them appear blurry, and (2) partial occlusion, which frequently occurs. This study proposes the mean angle of sperm motion and Tracking-Grid to predict the position of the sperm that failed to be detected. The Tracking-Grid has also been found useful in tracking fast-moving sperm. The proposed methods reduce identity switch (ID-switch) and achieve a multi-object tracking overall accuracy (MOTAL) of 73.2. The MOTAL result exhibits 5% less ID-switch and is 15.6 MOTAL points higher than state-of-the-art simple online and real-time tracking with a deep association metric (Deep SORT). The speed achieved is 41.18 frames per second (fps), which is 1.8 times faster than Deep SORT. In sperm motility classification, most researchers use one or several CASA parameters with a static threshold value. Such a method is effective for motile-progressive sperm classification but is less reliable for identifying non-motile-progressive sperm such as vibrating and floating sperm. This study proposes a machine learning-based motility classifier using a support vector machine with three CASA parameters: curvilinear velocity (VCL), straight-line velocity (VSL), and linearity (LIN), which we call the bull sperm progressive motility classifier (BSPMCsvm3casa). Experimental results show that BSPMCsvm3casa’s mean accuracy is 92.08%, which is 2.51–9.67 points higher than other classification methods.