Multistage Optimization Using a Modified Gaussian Mixture Model in Sperm Motility Tracking

Alameri, Mohammed; Hasikin‬, Khairunnisa; Kadri, Nahrizul Adib; Nasir, N. F. Mohd; Mohandas, Prabu; Anni, Jerline Sheeba; Azizan, Muhammad Mokhzaini

doi:10.1155/2021/6953593

Cited by 4 publications

(7 citation statements)

References 36 publications

(27 reference statements)

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“…In their paper, research conducted by Alameri, et al (2021) proposed a modified GMM algorithm to optimize the detection and tracking process of multi-sperm spermatozoa [38]. The proposed method consists of two stages: the first stage focuses on accurate sperm detection, while the second stage involves sperm tracking and velocity measurement.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed method consists of two stages: the first stage focuses on accurate sperm detection, while the second stage involves sperm tracking and velocity measurement. Motility results were evaluated on 10 sperm samples, and the performance of the proposed method was compared with several other methods [38]. When tested on 10 sperm motility videos, consisting of, 4 samples tested were classified as normal and the other 6 were considered abnormal.…”

Section: Introductionmentioning

confidence: 99%

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Abnormality Determination of Spermatozoa Motility Using Gaussian Mixture Model and Matching-based Algorithm

Mas Diyasa,

Saputra,

Gunawan

et al. 2024

Journal of Robotics and Control

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Sperm analysis is an initial step in the examination conducted to identify infertility cases in humans. One aspect of sperm analysis involves observing the movement of spermatozoa and determining whether it is normal or abnormal. Normal spermatozoa movement is characterized by progressive motion at an average speed of 20 µm/second, while abnormal movement includes slow or non-motile spermatozoa. Traditional methods can be employed to assess the normality or abnormality of sperm movement, but they have drawbacks such as time-consuming procedures and diverse results depending on the expertise of the examiner. On the other hand, utilizing Computer-Assisted Sperm Analysis (CASA) equipment provides consistent results, albeit at a relatively high cost. Therefore, this research proposes an alternative method for determining sperm movement abnormalities using the Gaussian Mixture Model (GMM) for background subtraction and a matching-based algorithm to track and analyze the formed trajectories, distinguishing between normal and abnormal sperm movement. Human spermatozoa in real-time are used, and their movements are recorded in video format using a bright field microscope. The testing results for determining sperm movement abnormalities based on the GMM method and matching-based algorithm were successful, particularly in videos recorded at 50 fps recording speed, 20 minutes of liquefaction time, and 40x microscope lens magnification. This condition exhibited the highest average accuracy, with a tracking accuracy of 77.3% and an average accuracy for determining sperm motility abnormalities of 87.7%. Therefore, the combined tracking of sperm movement based on the GMM method and matching-based algorithm can be utilized to identify abnormalities in the movement of human spermatozoa.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Abnormality Determination of Spermatozoa Motility Using Gaussian Mixture Model and Matching-based Algorithm

Mas Diyasa,

Saputra,

Gunawan

et al. 2024

Journal of Robotics and Control

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“…When motile sperm collide with impurities with similar contrast, it is easy to lose the sperm target, resulting in a certain percentage of sperm-tracking loss. Alameri et al [ 21 ] adopted an improved Gaussian mixture model to detect sperm and automatically update, including new sperm that re-entered the field of vision. When the number of swimming sperm is large enough, the calculation speed of the algorithm is greatly reduced.…”

Section: Introductionmentioning

confidence: 99%

YOLOv5s-SA: Light-Weighted and Improved YOLOv5s for Sperm Detection

et al. 2023

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Sperm detection performance is particularly critical for sperm motility tracking. However, there are a large number of non-sperm objects, sperm occlusion and poorly detailed texture features in semen images, which directly affect the accuracy of sperm detection. To solve the problem of false detection and missed detection in sperm detection, a multi-sperm target detection model, Yolov5s-SA, with an SA attention mechanism is proposed based on the YOLOv5s algorithm. Firstly, a depthwise, separable convolution structure is used to replace the partial convolution of the backbone network, which can ensure stable precision and reduce the number of model parameters. Secondly, a new multi-scale feature fusion module is designed to enhance the perception of feature information to supplement the positional information and high-resolution of the deep feature map. Finally, the SA attention mechanism is integrated into the neck network before the output of the feature map to enhance the correlation between the feature map channels and improve the fine-grained feature fusion ability of YOLOv5s. Experimental results show that compared with various YOLO algorithms, the proposed algorithm improves the detection accuracy and speed to a certain extent. Compared with the YOLOv3, YOLOv3-spp, YOLOv5s and YOLOv5m models, the average accuracy increases by 18.1%, 15.2%, 6.9% and 1.9%, respectively. It can effectively reduce the missed detection of occluded sperm and achieve lightweight and efficient multi-sperm target detection.

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“…Most of the work has focused on sperm morphology to improve the accuracy of the assessment and make the classification automated. There have also been several approaches to developing machine learning models for tracking sperm movement, however, few studies have investigated how algorithms are able to predict sperm motility [9][10][11][12][13][14] .…”

mentioning

confidence: 99%

“…The data in that study was based on manual assessment of three categories of motility as described in WHO 2010 Manual 15 , performed by one person. This dataset has also been used in two recent studies 12,13 . According to the WHO recommendations from 2010, the assessment of sperm motility is based on a system that distinguishes motile spermatozoa from immotile and progressive motility from non-progressive.…”

mentioning

confidence: 99%

Sperm motility assessed by deep convolutional neural networks into WHO categories

Haugen,

Witczak,

Hicks

et al. 2023

Sci Rep

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Semen analysis is central in infertility investigation. Manual assessment of sperm motility according to the WHO recommendations is the golden standard, and extensive training is a requirement for accurate and reproducible results. Deep convolutional neural networks (DCNN) are especially suitable for image classification. In this study, we evaluated the performance of the DCNN ResNet-50 in predicting the proportion of sperm in the WHO motility categories. Two models were evaluated using tenfold cross-validation with 65 video recordings of wet semen preparations from an external quality assessment programme for semen analysis. The corresponding manually assessed data was obtained from several of the reference laboratories, and the mean values were used for training of the DCNN models. One model was trained to predict the three categories progressive motility, non-progressive motility, and immotile spermatozoa. Another model was used in predicting four categories, where progressive motility was differentiated into rapid and slow. The resulting average mean absolute error (MAE) was 0.05 and 0.07, and the average ZeroR baseline was 0.09 and 0.10 for the three-category and the four-category model, respectively. Manual and DCNN-predicted motility was compared by Pearson’s correlation coefficient and by difference plots. The strongest correlation between the mean manually assessed values and DCNN-predicted motility was observed for % progressively motile spermatozoa (Pearson’s r = 0.88, p < 0.001) and % immotile spermatozoa (r = 0.89, p < 0.001). For rapid progressive motility, the correlation was moderate (Pearson’s r = 0.673, p < 0.001). The median difference between manual and predicted progressive motility was 0 and 2 for immotile spermatozoa. The largest bias was observed at high and low percentages of progressive and immotile spermatozoa. The DCNN-predicted value was within the range of the interlaboratory variation of the results for most of the samples. In conclusion, DCNN models were able to predict the proportion of spermatozoa into the WHO motility categories with significantly lower error than the baseline. The best correlation between the manual and the DCNN-predicted motility values was found for the categories progressive and immotile. Of note, there was considerable variation between the mean motility values obtained for each category by the reference laboratories, especially for rapid progressive motility, which impacts the training of the DCNN models.

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Multistage Optimization Using a Modified Gaussian Mixture Model in Sperm Motility Tracking

Cited by 4 publications

References 36 publications

Abnormality Determination of Spermatozoa Motility Using Gaussian Mixture Model and Matching-based Algorithm

Abnormality Determination of Spermatozoa Motility Using Gaussian Mixture Model and Matching-based Algorithm

YOLOv5s-SA: Light-Weighted and Improved YOLOv5s for Sperm Detection

Sperm motility assessed by deep convolutional neural networks into WHO categories

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