Using polynomial modeling for calculation of quality parameters in computer assisted sperm analysis

Abstract

Measurement of sperm velocity parameters takes an important place in sperm analysis. Today, computer-assisted sperm analysis (CASA) systems are used to detect motility measurements such as VCL (Curvilinear Velocity), VSL (Linear Velocity) and VAP (Average Path Velocity). The path length of sperm cells is necessary to calculate the motility parameters and it is calculated using video processing. However, this path length is obtained by the discrete-time processing of video frames, which can lead to unrealistic results. In CASA systems, the frame rate of the videos is increased to obtain the natural path length, but in this case, it is necessary to increase the hardware cost as well. Polynomial modeling applied to measure the natural path length in discrete-time structures can solve this problem. In this study, Lagrange interpolation which is an effective polynomial modeling method was used to obtain the natural path length and velocity parameters of the sperm. The results of the study showed that, when applied the polynomial modeling to calculate VCL and VAP parameters, it was found that especially in the low frame rate, more effective data were obtained than the classical method. As a result of this study, it is recommended to use polynomial modeling in the sperm velocity calculations of CASA systems, although there is an increase in calculation time.

Keywords

• Tracking (time series analysis)
• Polynomial modeling
• Sperm motility
• Lagrange interpolation
• Computer-assisted detection and diagnosis.

Using polynomial modeling for calculation of quality parameters in computer assisted sperm analysis

Abstract

Sperm hız parametrelerinin ölçümü sperm analizinde önemli bir yer tutar. Günümüzde VCL (Eğrisel Hız), VSL (Doğrusal Hız) ve VAP (Ortalama Yol Hızı) gibi motilite ölçümlerini tespit etmek için bilgisayar destekli sperm analizi (CASA) sistemleri kullanılmaktadır. Motilite parametrelerini hesaplamak için sperm hücrelerinin yol uzunluğu gereklidir ve video işleme kullanılarak hesaplanır. Ancak bu yol uzunluğu, video karelerinin ayrık zamanlı işlenmesiyle elde edilir ve bu da gerçekçi olmayan sonuçlara yol açabilir. CASA sistemlerinde doğal yol uzunluğunu elde etmek için videoların kare hızı artırılır ancak bu durumda donanım maliyetini de artırmak gerekir. Ayrık zamanlı yapılarda doğal yol uzunluğunu ölçmek için uygulanan polinom modelleme bu sorunu çözebilir. Bu çalışmada, spermin doğal yol uzunluğu ve hız parametrelerini elde etmek için etkili bir polinom modelleme yöntemi olan Lagrange interpolasyonu kullanılmıştır. Çalışmanın sonuçları, VCL ve VAP parametrelerini hesaplamak için polinom modellemesi uygulandığında, özellikle düşük kare hızında klasik yönteme göre daha etkili veriler elde edildiğini göstermiştir. Bu çalışma sonucunda hesaplama süresinde artış olmasına rağmen, CASA sistemlerinin sperm hızı hesaplamalarında polinom modellemesinin kullanılması önerilmektedir.

Keywords

• Zaman serisi analizi
• Polinom modelleme
• Sperm hareketliliği
• Lagrange enterpolasyonu
• Bilgisayar destekli tespit ve teşhis.

References

1. Ahn, H., Cho, H.-J., 2019. Research of multi-object detection and tracking using machine learning based on knowledge for video surveillance system. Pers. Ubiquitous Comput. https://doi.org/10.1007/s00779-019-01296-z
2. Alquézar-Baeta, C., Gimeno-Martos, S., Miguel-Jiménez, S., Santolaria, P., Yániz, J., Palacín, I., Casao, A., Cebrián-Pérez, J.Á., Muiño-Blanco, T., Pérez-Pé, R., 2019. OpenCASA: A new open-source and scalable tool for sperm quality analysis. PLOS Comput. Biol. 15, e1006691. https://doi.org/10.1371/journal.pcbi.1006691
3. Amann, R.P., Waberski, D., 2014. Computer-assisted sperm analysis (CASA): Capabilities and potential developments. Theriogenology 81, 5-17.e3. https://doi.org/10.1016/j.theriogenology.2013.09.004
4. Boe-Hansen, G.B., Satake, N., 2019. An update on boar semen assessments by flow cytometry and CASA. Theriogenology. https://doi.org/10.1016/j.theriogenology.2019.05.043
5. Bompart, D., García-Molina, A., Valverde, A., Caldeira, C., Yániz, J., Núñez de Murga, M., Soler, C., 2018. CASA-Mot technology: how results are affected by the frame rate and counting chamber. Reprod. Fertil. Dev. 30, 810. https://doi.org/10.1071/RD17551
6. Bouwmans, T., 2014. Traditional and recent approaches in background modeling for foreground detection: An overview. Comput. Sci. Rev. https://doi.org/10.1016/j.cosrev.2014.04.001
7. Castellini, C., Dal Bosco, A., Ruggeri, S., Collodel, G., 2011. What is the best frame rate for evaluation of sperm motility in different species by computer-assisted sperm analysis? Fertil. Steril. 96, 24–27. https://doi.org/10.1016/J.FERTNSTERT.2011.04.096
8. Cocorullo, G., Corsonello, P., Frustaci, F., Guachi-Guachi, L. de los A., Perri, S., 2016. Multimodal background subtraction for high-performance embedded systems. J. Real-Time Image Process. 1–17. https://doi.org/10.1007/s11554-016-0651-6

9. Contri, A., Valorz, C., Faustini, M., Wegher, L., Carluccio, A., 2010. Effect of semen preparation on casa motility results in cryopreserved bull spermatozoa. Theriogenology 74, 424–435. https://doi.org/10.1016/j.theriogenology.2010.02.025
10. Duffy, B., Thiyagalingam, J., Walton, S., Smith, D.J., Trefethen, A., Kirkman-Brown, J.C., Gaffney, E.A., Chen, M., 2015. Glyph-Based Video Visualization for Semen Analysis. IEEE Trans. Vis. Comput. Graph. 21, 980–993. https://doi.org/10.1109/TVCG.2013.265
11. Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.S., 2002. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proc. IEEE 90, 1151–1162. https://doi.org/10.1109/JPROC.2002.801448
12. Hasan, M.S., Rahman, T., Islam, S.K., Blalock, B.B., 2017. Numerical modeling and implementation in circuit simulator of SOI four-gate transistor (G4FET) using multidimensional Lagrange and Bernstein polynomial. Microelectronics J. 65, 84–93. https://doi.org/10.1016/J.MEJO.2017.05.011
13. Hidayatullah, P., Awaludin, I., Kusumo, R.D., Nuriyadi, M., 2015. Automatic sperm motility measurement, in: 2015 International Conference on Information Technology Systems and Innovation (ICITSI). IEEE, pp. 1–5. https://doi.org/10.1109/ICITSI.2015.7437674
14. Hu, F., Fan, J., Luo, K., Zhou, Y., Wu, C., Luo, L., Wang, S., Tao, M., Zhang, C., Chen, B., Ma, M., Liu, S., 2019. Comparative analyses of reproductive characteristics of functional sex reversal male gynogenetic red crucian carp and ordinary male red crucian carp. Aquaculture 511. https://doi.org/10.1016/j.aquaculture.2019.06.013
15. Khalifa, N.E.M., Taha, M.H.N., Hassanien, A.E., 2019. Automatic Counting and Visual Multi-tracking System for Human Sperm in Microscopic Video Frames. Springer, Cham, pp. 525–531. https://doi.org/10.1007/978-3-319-99010-1_48
16. Křížková, J., Čoudková, V., Maršálek, M., 2017. Computer-Assisted Sperm Analysis of Head Morphometry and Kinematic Parameters in Warmblood Stallions Spermatozoa. J. Equine Vet. Sci. 57, 8–17. https://doi.org/10.1016/J.JEVS.2017.05.012
17. Lehmann, T.M., Gonner, C., Spitzer, K., 1999. Survey: interpolation methods in medical image processing. IEEE Trans. Med. Imaging 18, 1049–1075. https://doi.org/10.1109/42.816070
18. Lu, J.C., Huang, Y.F., Lü, N.Q., 2014. Computer-aided sperm analysis: past, present and future. Andrologia 46, 329–338. https://doi.org/10.1111/and.12093
19. Nieschlag, E., Behre, H.M., 2001. Andrology : Male Reproductive Health and Dysfunction. Springer Berlin Heidelberg.
20. Özgür, M.E., Balcıoğlu, S., Ulu, A., Özcan, İ., Okumuş, F., Köytepe, S., Ateş, B., 2018. The in vitro toxicity analysis of titanium dioxide (TiO 2 ) nanoparticles on kinematics and biochemical quality of rainbow trout sperm cells. Environ. Toxicol. Pharmacol. https://doi.org/10.1016/j.etap.2018.06.002
21. ÖZGÜR, M.E., OKUMUŞ, F., KOCAMAZ, A.F., 2019. A Novel Computer Assisted Sperm Analyzer for Assessment of Spermatozoa Motility in Fish; BASA-Sperm Aqua. El-Cezeri Fen ve Mühendislik Derg. 6, 208–219. https://doi.org/10.31202/ecjse.486342
22. Qi, S., Nie, T., Li, Q., He, Z., Xu, D., Chen, Q., 2019. A Sperm Cell Tracking Recognition and Classification Method. Institute of Electrical and Electronics Engineers (IEEE), pp. 163–167. https://doi.org/10.1109/iwssip.2019.8787312
23. Rurangwa, E., Kime, D.E., Ollevier, F., Nash, J.P., 2004. The measurement of sperm motility and factors affecting sperm quality in cultured fish. Aquaculture. https://doi.org/10.1016/j.aquaculture.2003.12.006
24. Scherer, P.O.J., 2013. Interpolation. pp. 15–35. https://doi.org/10.1007/978-3-319-00401-3_2
25. Sobral, A., Vacavant, A., 2014. A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos. Comput. Vis. Image Underst. 122, 4–21. https://doi.org/10.1016/j.cviu.2013.12.005
26. Stauffer, C., Grimson, W.E.L., 1999. Adaptive background mixture models for real-time tracking. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2, 246–252. https://doi.org/10.1109/cvpr.1999.784637
27. Sun, Y., Xiong, Z., 2017. High-order full-discretization method using Lagrange interpolation for stability analysis of turning processes with stiffness variation. J. Sound Vib. 386, 50–64. https://doi.org/10.1016/J.JSV.2016.08.039
28. Urbano, L.F., Masson, P., VerMilyea, M., Kam, M., 2017. Automatic Tracking and Motility Analysis of Human Sperm in Time-Lapse Images. IEEE Trans. Med. Imaging 36, 792–801. https://doi.org/10.1109/TMI.2016.2630720
29. Valverde, A., Madrigal, M., Caldeira, C., Bompart, D., de Murga, J.N., Arnau, S., Soler, C., 2019. Effect of frame rate capture frequency on sperm kinematic parameters and subpopulation structure definition in boars, analysed with a CASA-Mot system. Reprod. Domest. Anim. 54, 167–175. https://doi.org/10.1111/rda.13320
30. Wilson-Leedy, J.G., Ingermann, R.L., 2007. Development of a novel CASA system based on open source software for characterization of zebrafish sperm motility parameters. Theriogenology 67, 661–672. https://doi.org/10.1016/J.THERIOGENOLOGY.2006.10.003
31. World Health Organization, 2010. WHO laboratory manual for the Examination and processing of human semen, World Health Organization. https://doi.org/10.1038/aja.2008.57
32. Yamasaki, K., Watanabe, N., Ihana, T., Ishijima, S., Fujiwara, T., Tsutsumi, O., Iwamoto, T., 2017. MP07-10 USEFULNESS OF A PORTABLE COMPUTER-ASSISTED SPERM ANALYZER SYSTEM USING SMARTPHONE. J. Urol. 197. https://doi.org/10.1016/j.juro.2017.02.276
33. Yániz, J.L., Palacín, I., Vicente-Fiel, S., Sánchez-Nadal, J.A., Santolaria, P., 2015. Sperm population structure in high and low field fertility rams. Anim. Reprod. Sci. 156, 128–134. https://doi.org/10.1016/J.ANIREPROSCI.2015.03.012