Real-time tracing of spermatozoa

Young, Shuenn‐Tsong; Tzeng, Wen-Guey; Kuo, Y.L.; Hsiao, Ming-Liang; Chiang, Szu‐Hui

doi:10.1109/51.544519

Cited by 11 publications

(6 citation statements)

References 16 publications

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“…There are many studies focusing on the estimation of spermatozoa movement parameters from a few decades ago 12–14 . Some studies focus on single cell tracking 14–16 and the others concentrate on multiple cell tracking 11,17,18 .…”

Section: Introductionmentioning

confidence: 99%

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Multi-Target Tracking of Human Spermatozoa in Phase-Contrast Microscopy Image Sequences using a Hybrid Dynamic Bayesian Network

Arasteh

Vahdat

Yazdi

2018

Sci Rep

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Male infertility is mostly related to semen and spermatozoa, and any diagnosis or treatment requires the investigation of the motility patterns of spermatozoa. The movements of spermatozoa are fast and involve collision and occlusion with each other. In order to extract the motility patterns of spermatozoa, multi-target tracking (MTT) of spermatozoa is necessary. One of the most important steps of MTT is data association, in which the newly arrived observations are used to update the previous tracks. Dynamic Bayesian network (DBN) is a powerful tool for modeling and solving various types of problems such as tracking and classification. There can also be a hybrid-DBN (HDBN), in which both continuous and discrete nodes are present. HDBN has a suitable structure for modeling problems that have both discrete and continuous parameters like MTT. In this research, the data association for MTT of human spermatozoa has been studied. The proposed algorithm was tested over hundreds of manually extracted spermatozoa tracks and evaluated using several standard measures. The superior results of the proposed algorithm in comparison to the other well-known algorithms, show that it could be considered as an improved alternative to traditional computer assisted sperm analysis (CASA) algorithms.

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Section: Introductionmentioning

confidence: 99%

“…Some studies focus on single cell tracking 14–16 and the others concentrate on multiple cell tracking 11,17,18 . It is obvious that tracking multiple cells at once is harder than tracking just a single cell.…”

Section: Introductionmentioning

confidence: 99%

Multi-Target Tracking of Human Spermatozoa in Phase-Contrast Microscopy Image Sequences using a Hybrid Dynamic Bayesian Network

Arasteh

Vahdat

Yazdi

2018

Sci Rep

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“…These systems have been commercially available since the mid-1980s and a detailed review of CASA can be found in Amann and Katz (2004) and Mortimer (1994). In order to further speed up the automated measurable parameters of sperm motility, Young et al (1996) and Kuo et al (2000) developed a real-time automated-stage sperm tracking system to analyze highly dilute sperm suspensions at 30 frames/second.…”

Section: Introductionmentioning

confidence: 99%

An automatic system to study sperm motility and energetics

Shi

Nascimento

Chandsawangbhuwana

et al. 2008

Biomed Microdevices

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An integrated robotic laser and microscope system has been developed to automatically analyze individual sperm motility and energetics. The custom-designed optical system directs near-infrared laser light into an inverted microscope to create a single-point 3-D gradient laser trap at the focal spot of the microscope objective. A two-level computer structure is described that quantifies the sperm motility (in terms of swimming speed and swimming force) and energetics (measuring mid-piece membrane potential) using real-time tracking (done by the upper-level system) and fluorescent ratio imaging (done by the lower-level system). The communication between these two systems is achieved by a gigabit network. The custom-built image processing algorithm identifies the sperm swimming trajectory in real-time using phase contrast images, and then subsequently traps the sperm by automatically moving the microscope stage to relocate the sperm to the laser trap focal plane. Once the sperm is stably trapped (determined by the algorithm), the algorithm can also gradually reduce the laser power by rotating the polarizer in the laser path to measure the trapping power at which the sperm is capable of escaping the trap. To monitor the membrane potential of the mitochondria located in a sperm’s mid-piece, the sperm is treated with a ratiometrically-encoded fluorescent probe. The proposed algorithm can relocate the sperm to the center of the ratio imaging camera and the average ratio value can be measured in real-time. The three parameters, sperm escape power, sperm swimming speed and ratio values of the mid-piece membrane potential of individual sperm can be compared with respect to time. This two-level automatic system to study individual sperm motility and energetics has not only increased experimental throughput by an order of magnitude but also has allowed us to monitor sperm energetics prior to and after exposure to the laser trap. This system should have application in both the human fertility clinic and in animal husbandry.

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“…The easiest way to segment colloids or cells from the background is to set a manually or automatically defined threshold that removes the background noise. This approach was applied successfully in many particle tracking algorithms [144,145,146,147]. Prior to threshold definition, it can be necessary to increase the image signal to noise ratio (SNR) by image processing.…”

Section: Introductionmentioning

confidence: 99%