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Automated human sperm tracking using mean shift - collision detection and modified covariance matrix method

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Abstract

In vitro fertilisation (IVF) is a popular technique in assisted reproductive technology. The success of IVF mainly depends on the selection of the correct sperm in human semen sample. Sperm tracking plays an important role in selecting the active-moving sperm. One of the major challenges in sperm tracking is the collision of sperm cases during tracking. To solve this issue, mean shift–collision detection and modified covariance matrix (MS–CDMCM) is proposed. Specifically, MS–CDMCM detects collision and generates a new covariance matrix based on the collision condition. Then, this new covariance matrix will form a new tracked region to continue the tracking process. Results show that the proposed method is a more accurate and robust tracking method than other state-of-the-art sperm tracking methods. The proposed method produces significantly low error values, such as MAE, MSE and RMSE, according to the quantitative analysis when compared with ground truth images. The proposed method is expected to be implemented in sperm motility assessment in the future.

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Acknowledgements

This study is supported by the Universiti Sains Malaysia through the Research University Grants (RUI) entitled ‘Development of Automated Intelligent Karyotyping System for Classifying Abnormal Chromosome’ 1001/PELECT/8014030 and by the Ministry of Higher Education under the MyPhD Scholarship. Credits are given to Nur Syuhada Mohd Nafis for her assistance in taking the sperm videos.

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Authors and Affiliations

  1. Imaging and Intelligent Systems Research Team (ISRT), School of Electrical and Electronic Engineering, Universiti Sains Malaysia, 14300, Nibong Tebal, Penang, Malaysia

    Weng Chun Tan & Mat Isa Nor Ashidi

  2. Department of Physiology, School of Medical Sciences, Universiti Sains Malaysia, 16150, Kubang Kerian, Malaysia

    Mahaneem Mohamed

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  1. Weng Chun Tan
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  2. Mat Isa Nor Ashidi
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  3. Mahaneem Mohamed
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Correspondence to Mat Isa Nor Ashidi.

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Tan, W.C., Mat Isa, N.A. & Mohamed, M. Automated human sperm tracking using mean shift - collision detection and modified covariance matrix method. Multimed Tools Appl 79, 28551–28585 (2020). https://doi.org/10.1007/s11042-020-09396-2

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  • DOI: https://doi.org/10.1007/s11042-020-09396-2

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