Head tracking and flagellum tracing for sperm motility analysis

Abstract

Sperm quality assessment plays an essential role in human fertility and animal breeding. Manual analysis is time-consuming and subject to intra-and inter-observer variability. To automate the analysis process, as well as to offer a means of statistical analysis that may not be achieved by visual inspection, we present a computational framework that tracks the heads and traces the tails for analyzing sperm motility, one of the most important attributes in semen quality evaluation. Our framework consists of 3 modules: head detection, head tracking, and flagellum tracing. The head detection module detects the sperm heads from the image data, and the detected heads are the inputs to the head tracking module for obtaining the head trajectories. Finally, a flagellum tracing algorithm is proposed to obtain the flagellar beat patterns. Our framework aims at providing both the head trajectories and the flagellar beat patterns for quantitatively assessing sperm motility. This distinguishes our work from other existing methods that analyze sperm motility based merely on the head trajectories. We validate our framework using two confocal microscopy image sequences of ram semen samples that were imaged at two different conditions, at which the sperms behave differently. The results show the effectiveness of our framework.

Modelling Motility: The Mathematics of Spermatozoa

“…More recently, a wide range of refined semi-automatic schemes, requiring further-reduced user input, and even fully automatic unsupervised methods, have become available for tracing out a slender flagellum-like object from videomicroscopy. A selection of these approaches are tailored to the morphology and characteristics of spermatozoa (Smith et al, 2009a ; Yang et al, 2014a ; Hansen et al, 2018 ; Gallagher et al, 2019 ), whilst others are somewhat more general (Hongsheng et al, 2009 ; Goldstein et al, 2010 ; Xu et al, 2014 ; Xiao et al, 2016 ; Walker et al, 2019c ); an example output of one of the latter techniques is reproduced in Figure 1A . The development of these software tools and approaches, in combination with improvements in the fidelity of videomicroscopy, has newly enabled studies at scale of the details of flagellar beating in a variety of organisms, including bovine and human spermatozoa (Gallagher et al, 2019 ; Walker et al, 2019d , 2020b ), each analysing hundreds of individual swimmers, with the potential for significant future application and extension.…”

A Bag of Features Based Approach for Classification of Motile Sperm Cells

“…The HOG is based on the distribution of intensities of gradients G x and G y of a given image. Gradient estimates at a pixel(i, j) are given by (1) and (2).…”

“…Nowadays, analysis of sperm cells remains subjective, imprecise, and highly time-consuming task [2]. A health professional, usually using a microscope, counts and evaluates the morphology of cells following guidelines established by the WHO [1] and based on their own experience.…”

Occlusion Robust Low-Contrast Sperm Tracking Using Switchable Weight Particle Filtering

“…In another aspect, it has been tried to develop algorithms in order to achieve more accurate and robust sperm trackers. Using visual evaluation of microscopic field [10], template matching [11] [12], particle and Kalman filters [13] [14], nearest neighbor technique [15], time differential method [16], optimal matching [17] co-registration process based on block matching [18], high-speed visual feedback [19], and optical flow [20] are some tracking algorithms for sperm cells employed so far.…”