Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-23T08:24:22.945Z Has data issue: false hasContentIssue false

Chapter 6 - Computer-Aided Sperm Analysis

Published online by Cambridge University Press:  16 February 2022

David Mortimer ,
Lars Björndahl ,
Christopher L. R. Barratt ,
José Antonio Castilla ,
Roelof Menkveld ,
Ulrik Kvist ,
Juan G. Alvarez  and
Trine B. Haugen
David Mortimer
Affiliation:
Oozoa Biomedical Inc., Vancouver
Lars Björndahl
Affiliation:
Karolinska Institutet, Stockholm
Christopher L. R. Barratt
Affiliation:
University of Dundee
José Antonio Castilla
Affiliation:
HU Virgen de las Nieves, Granada
Roelof Menkveld
Affiliation:
Stellenbosch University, South Africa
Ulrik Kvist
Affiliation:
Karolinska Institutet, Stockholm
Juan G. Alvarez
Affiliation:
Centro ANDROGEN, La Coruña
Trine B. Haugen
Affiliation:
Oslo Metropolitan University
Get access

Summary

Discusses the development, application and limitations of computer-aided sperm analysis (CASA) methods, including the deriation of kinematic measures of human sperm motility. Explains the technical and biological factors that limit CASA's functionality for human semen analysis and summarizes expert recommendations on the use of CASA for human semen analysis and sperm kinematics analysis (including sperm-mucus penetration and sperm hyperactivation). Issues related to the non-comparability of different CASA systems are considered, along with quality control for CASA. A strategy for validating a CASA system for human semen analysis, based on expectations of accuracy and precision, is also provided. Finally the use of CASA for analyzing sperm function tests, and new and future CASA technology (including the application of artificial intelligence technqiues) are surveyed.

Keywords

computer-aided sperm analysisCASAsemen analysissperm motilitysperm kinematicshyperactivation
Type
Chapter
Information
A Practical Guide to Basic Laboratory Andrology , pp. 130 - 154
Publisher: Cambridge University Press
Print publication year: 2022

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

MacLeod, J, Gold, RZ. The male factor in fertility and infertility. III. An analysis of motile activity in the spermatozoa of 1000 fertile men and 1000 men in infertile marriage. Fertil Steril 1951; 2: 187–204.Google Scholar
Mortimer, D. Practical Laboratory Andrology. New York: Oxford University Press, 1994.CrossRefGoogle Scholar
Davis, RO, Katz, DF. Computer-aided sperm analysis: technology at a crossroads. Fertil Steril 1993; 59: 953–5.Google Scholar
Boyers, SP, Davis, RO, Katz, DF. Automated semen analysis. Curr Probl Obstet Gynecol Fertil 1989; XII: 167–200.Google Scholar
Mortimer, D. Objective analysis of sperm motility and kinematics. In: Keel, BA, Webster, BW, eds. Handbook of the Laboratory Diagnosis and Treatment of Infertility. Boca Raton: CRC Press, 1990.Google Scholar
Mortimer, ST. A critical review of the physiological importance and analysis of sperm movement in mammals. Hum Reprod Update 1997; 3: 403–39.CrossRefGoogle ScholarPubMed
Mortimer, D, Mortimer, ST. Value and reliability of CASA systems. In: Ombelet, W, Bosmans, E, Vandeput, H, et al., eds. Modern ART in the 2000s. Carnforth: Parthenon Publishing, 1998.Google Scholar
Mortimer, ST. CASA – practical aspects. J Androl 2000; 21: 515–24.CrossRefGoogle ScholarPubMed
Amann, RP, Waberski, D. Computer-assisted sperm analysis (CASA): capabilities and potential developments. Theriogenology 2014; 81: 5–17.CrossRefGoogle ScholarPubMed
Tomlinson, MJ, Naeem, A. CASA in the medical laboratory: CASA in diagnostic andrology and assisted conception. Reprod Fertil Dev 2018; 30: 850–9.CrossRefGoogle ScholarPubMed
Mortimer, D, Aitken, RJ, Mortimer, ST, Pacey, AA. Workshop report: Clinical CASA – The quest for consensus. Reprod Fertil Dev 1995; 7: 951–9.Google Scholar
ESHRE Andrology Special Interest Group. Consensus workshop on advanced diagnostic andrology techniques. Hum Reprod 1996; 11: 1463–79.Google Scholar
ESHRE Andrology Special Interest Group. Guidelines on the application of CASA technology in the analysis of spermatozoa. Hum Reprod 1998; 13: 142–5.Google Scholar
Mortimer, ST, van der Horst, G, Mortimer, D. The future of computer-aided sperm analysis. Asian J Androl 2015; 17: 545–53.Google Scholar
Mortimer, D, Mortimer, ST. Routine application of CASA in human clinical andrology and ART laboratories. In: Björndahl, L, Flanagan, J, Holmberg, R, Kvist, U, eds. XIIIth International Symposium on Spermatology. Switzerland: Springer Nature, 2021, 183–97.Google Scholar
Lippi, G, Da Rin, G. Advantages and limitations of total laboratory automation: a personal overview. Clin Chem Lab Med 2019; 57: 802–11.CrossRefGoogle ScholarPubMed
Thomson, RB Jr, McElvania, E. Total laboratory automation: what is gained, what is lost, and who can afford it? Clin Lab Med 2019 39: 371–89.Google Scholar
Serres, C, Feneux, D, Jouannet, P, David, G. Influence of the flagellar wave development and propagation on the human sperm movement in seminal plasma. Gamete Res 1984; 9: 183–95.CrossRefGoogle Scholar
Phillips, DM. Comparative analysis of mammalian sperm motility. J Cell Biol 1972; 53: 561–73.CrossRefGoogle ScholarPubMed
Woolley, DM. Interpretation of the pattern of sperm tail movements. In: Fawcett, DW, Bedford, JM, eds. The Spermatozoon. Maturation, Motility, Surface Properties and Comparative Aspects. Baltimore: Urban & Schwarzenberg, 1979.Google Scholar
Woolley, DM. A method for determining the three-dimensional form of active flagella, using two-colour darkground illumination. J Microsc 1981; 121: 241–4.CrossRefGoogle ScholarPubMed
Phillips, DM. The direction of rolling in mammalian spermatozoa. In: Andre, J, ed. The Sperm Cell. Fertilizing Power, Surface Properties, Motility, Nucleus and Acrosome, Evolutionary Aspects. The Hague: Martinus Nijhoff, 1983.Google Scholar
Serres, C, Escalier, D, David, G. Ultrastructural morphometry of the human sperm flagellum with a stereological analysis of the lengths of the dense fibres. Biol Cell 1983; 49: 153–62.Google ScholarPubMed
Denehy, MA. Herbison-Evans, D, Denehy, BV. Rotational and oscillatory components of the tailwave in ram spermatozoa. Biol Reprod 1975; 13: 289–97.CrossRefGoogle ScholarPubMed
Purcell, EM. Life at low Reynolds number. Am J Phys 1977; 45: s3–11.CrossRefGoogle Scholar
Corkidi, G, Taboada, B, Wood, CD, et al. Tracking sperm in three-dimensions. Biochem Biophys Res Commun 2008; 373: 125–9.CrossRefGoogle ScholarPubMed
Su, T-W, Choi, I, Feng, J, et al. Sperm trajectories form chiral ribbons. Sci Rep 2013; 3: 1664. https://doi.org/10.1038/srep01664CrossRefGoogle ScholarPubMed
Mortimer, D, Serres, C, Mortimer, ST, Jouannet, P. Influence of image sampling frequency on the perceived movement characteristics of progressively motile human spermatozoa. Gamete Res 1988; 20: 313–27.Google Scholar
Castellini, C, Dal Bosco, A, Ruggeri, S, Collodel, G. What is the best frame rate for evaluation of sperm motility in different species by computer-assisted sperm analysis? Fertil Steril 2011; 96: 24–7.Google Scholar
Mortimer, ST, Mortimer, D. Kinematics of human spermatozoa incubated under capacitating conditions. J Androl 1990; 11: 195–203.Google Scholar
Mortimer, ST, Schoëvaërt, D, Swan, MA, Mortimer, D. Quantitative observations of flagellar motility of capacitating human spermatozoa. Hum Reprod 1997; 12: 1006–12.Google Scholar
Mortimer, D, Mortimer, ST. The future of computer-assisted semen analysis in the evaluation of male infertility. In: Patuszak, A, Hotaling, J, Carrell, D, eds. Comprehensive Guide to Modern Andrology. Cambridge: Cambridge University Press, 2021, (in press).Google Scholar
Katz, MJ, George, EB. Fractals and the analysis of growth paths. Bull Math Biol 1985; 47: 273–86.Google Scholar
Mortimer, ST, Swan, MA. Effect of image sampling frequency on established and smoothing-independent kinematic values of capacitating human spermatozoa. J Androl 1999; 14: 997–1004.Google ScholarPubMed
Davis, RO, Siemers, RJ. Derivation and reliability of kinematic measures of sperm motion. Reprod Fertil Dev 1995; 7: 857–69.Google Scholar
Mortimer, ST, Swan, MA. The development of smoothing-independent kinematic measures of capacitating human sperm movement. Hum Reprod 1999; 14: 986–96.Google Scholar
Mortimer, ST. Minimum sperm trajectory length for reliable determination of the fractal dimension. Reprod Fertil Dev 1998; 10: 465–9.CrossRefGoogle ScholarPubMed
Mortimer, ST, Swan, MA, Mortimer, D. Fractal analysis of capacitating human spermatozoa. Hum Reprod 1996; 11: 1049–54.CrossRefGoogle ScholarPubMed
Mortimer, D. Sperm transport in the female genital tract. In: Grudzinskas, JG, Yovich, JL, eds. Cambridge Reviews in Human Reproduction, Volume 2: Gametes – The Spermatozoon. Cambridge: Cambridge University Press, 1995.Google Scholar
Schubert, B, Badiou, M, Force, A. Computer-aided sperm analysis, the new key player in routine sperm assessment. Andrologia 2019; 51: e13417.CrossRefGoogle ScholarPubMed
Soler, C, Picazo-Bueno, JÁ, Micó, V, et al. Effect of counting chamber depth on the accuracy of lensless microscopy for the assessment of boar sperm motility. Reprod Fertil Dev 2018 30: 924–34.CrossRefGoogle ScholarPubMed
Douglas-Hamilton, DH, Smith, NG, Kuster, CE, et al. Particle distribution in low-volume capillary-loaded chambers. J Androl 2005; 26: 107–14.CrossRefGoogle ScholarPubMed
Douglas-Hamilton, DH, Smith, NG, Kuster, CE, et al. Capillary-loaded particle fluid dynamics: effect on estimation of sperm concentration. J Androl 2005; 26: 115–22.CrossRefGoogle ScholarPubMed
Lammers, J, Splingart, C, Barrière, P, Jean, M, Fréour, T. Double-blind prospective study comparing two automated sperm analyzers versus manual semen assessment. J Assist Reprod Genet 2014; 31: 35–43.Google Scholar
Rijnders, S, Bolscher, JG, McDonnell, J, Vermeiden, JP. Filling time of a lamellar capillary-filling semen analysis chamber is a rapid, precise, and accurate method to assess viscosity of seminal plasma. J Androl 2007; 28: 461–5.CrossRefGoogle ScholarPubMed
van der Horst, G, Maree, L, du Plessis SS. Current perspectives of CASA applications in diverse mammalian spermatozoa. Reprod Fertil Dev 2018; 30: 875–88.CrossRefGoogle ScholarPubMed
Valverde, A, Castro-Morales, O, Madrigal-Valverde, M, Soler, C. Sperm kinematics and morphometric subpopulations analysis with CASA systems: a review. Int J Trop Biol 2019; 67: 1473–87.Google Scholar
Milligan, MP, Harris, SJ, Dennis, KJ. The effect of temperature on the velocity of human spermatozoa as measured by time-lapse photography. Fertil Steril 1978; 30: 592–4.Google Scholar
Björndahl, L. What is normal semen quality? On the use and abuse of reference limits for the interpretation of semen analysis results. Hum Fertil 2011; 14: 179–86.CrossRefGoogle ScholarPubMed
Barratt, CLR, Björndahl, L, Menkveld, R, Mortimer, D. The ESHRE Special Interest Group for Andrology Basic Semen Analysis Course: a continued focus on accuracy, quality, efficiency and clinical relevance. Hum Reprod 2011; 26: 3207–12.Google Scholar
Mortimer, D, Mortimer, ST. Laboratory investigation of the infertile male. In: Brinsden, PR, ed. A Textbook of In-Vitro Fertilization and Assisted Reproduction, 3rd edn. London: Taylor & Francis Medical Books, 2005.Google Scholar
Mortimer, D, Mortimer, ST. Computer-aided sperm analysis (CASA) of sperm motility and hyperactivation. In: Carrell, DT, Aston, KI, eds. Spermatogenesis and Spermiogenesis: Methods and Protocols. New York: Springer (Humana Press), 2013.Google Scholar
Mortimer, D. (1999) Structured management as a basis for cost-effective infertility care. In: Gagnon, C, ed. The Male Gamete: From Basic Knowledge to Clinical Applications. Vienna: Cache River Press, 1999.Google Scholar
Mortimer, D, Mortimer, ST. The case against intracytoplasmic sperm injection for all. In: Aitken, J, Mortimer, D, Kovacs, G, eds. Male and Sperm Factors that Maximize IVF Success. Cambridge: Cambridge University Press, 2020.Google Scholar
Zinaman, MJ, Uhler, ML, Vertuno, E, et al. Evaluation of computer-assisted semen analysis (CASA) with IDENT stain to determine sperm concentration. J Androl 1996; 17: 288–92.CrossRefGoogle ScholarPubMed
Rowe, PJ, Comhaire, FH, Hargreave, TB, Mahmoud, AMA. WHO Clinical Manual for the Standardized Investigation, Diagnosis and Management of the Infertile Male. Cambridge: Cambridge University Press, 2000.Google Scholar
World Health Organization. WHO Laboratory Manual for the Examination and Processing of Human Semen, 5th edn. Geneva: World Health Organization, 2010.Google Scholar
Mortimer, D, Menkveld, R. Sperm morphology assessment – Historical perspectives and current opinions. J Androl 2001; 22: 192–205.CrossRefGoogle ScholarPubMed
Mortimer, D. Sperm form and function: beauty is in the eye of the beholder. In: van der Horst, G, Franken, D, Bornman, R, de Jager, T, Dyer, S, eds. Proceedings of 9th International Symposium on Spermatology. Bologna: Monduzzi Editore, 2002, 257–62.Google Scholar
Auger, J, Jouannet, P, Eustache, F. Another look at human sperm morphology. Hum Reprod 2016; 31: 10–23.CrossRefGoogle Scholar
Gatimel, N, Moreau, J, Parinaud, J, Léandri, RD. Sperm morphology: assessment, pathophysiology, clinical relevance, and state of the art in 2017. Andrology 2017; 5: 845–62.Google Scholar
Mortimer, D. The functional anatomy of the human spermatozoon: relating ultrastructure and function. Mol Hum Reprod 2018; 24: 567–92.Google ScholarPubMed
Davis, RO, Gravance, CG. Standardization of specimen preparation, staining, and sampling methods improves automated sperm-head morphometry analysis. Fertil Steril 1993; 59: 412–17.Google Scholar
Lacquet, FA, Kruger, TF, Du Toit, TC, et al. Slide preparation and staining procedures for reliable results using computerized morphology. Arch Androl 1996; 36: 133–8.CrossRefGoogle ScholarPubMed
van der Horst, G, Maree, L. SpermBlue®: a new universal stain for human and animal sperm which is also amenable to automated sperm morphology analysis. Biotechnic Histochem 2009; 84: 299–308.Google Scholar
Maree, L, du Plessis, SS, Menkveld, RM, van der Horst, G. Morphometric dimensions of the human sperm head depend on the staining method used. Hum Reprod 2010; 25: 1369–82Google Scholar
Schoevaert, D. Automated recognition and morphological analysis of human spermatozoa. In: Robard, D, Forti, G, eds. Computers in Endocrinology. New York: Raven Press, 1984.Google Scholar
Yániz, JL, Soler, C, Santolaria, P. Computer-assisted sperm morphometry in mammals: a review. Anim Reprod Sci 2015; 156: 1–12.Google Scholar
Davis, RO, Thal, DM, Bain, DE, et al. Accuracy and precision of the CellForm-Human automated sperm morphometry instrument. Fertil Steril 1992; 58: 763–9.Google Scholar
Kruger, T. Computer-assisted sperm analysis systems: morphometric aspects. Hum Reprod 1995; 10 Suppl. 1: 46–52.CrossRefGoogle ScholarPubMed
Farrell, P, Trouern-Trend, V, Foote, RH, Douglas-Hamilton, D. Repeatability of measurements on human, rabbit, and bull sperm by computer-assisted sperm analysis when comparing individual fields and means of 12 fields. Fertil Steril 1995; 64: 208–10.Google Scholar
Hofmann, GE, Santilli, BA, Kindig, S, et al. Intraobserver, interobserver variation of sperm critical morphology: comparison of examiner and computer-assisted analysis. Fertil Steril 1996; 65: 1021–5.Google Scholar
Kruger, TF, Lacquet, FA, Sarmiento, CAS, et al. A prospective study on the predictive value of normal sperm morphology as evaluated by computer (IVOS). Fertil Steril 1996; 66: 285–91.CrossRefGoogle ScholarPubMed
Coetzee, K, Kruger, TF, Lombard, CJ. Repeatability and variance analysis on multiple computer-assisted (IVOS) sperm morphology readings. Andrologia 1999; 31: 163–8.Google Scholar
Soler, C, Gaßner, P, Nieschlag, E, et al. Utilización del Integrated Semen Analysis System (ISAS)® para el análisis morfométrico espermático humano y su significado en las técnicas de reproducción asistida. Revista Internacional de Andrología 2005; 3, 112–19.Google Scholar
Mortimer, D, Curtis, EF, Camenzind, AR. Combined use of fluorescent peanut agglutinin lectin and Hoechst 33258 to monitor the acrosomal status and vitality of human spermatozoa. Hum Reprod 1990; 5: 99–103CrossRefGoogle ScholarPubMed
Irvine, DS, Macleod, IC, Templeton, AA, et al. A prospective clinical study of the relationship between the computer-assisted assessment of human semen quality and the achievement of pregnancy in vivo. Hum Reprod 1994; 9: 2324–34.Google Scholar
Macleod, IC, Irvine, DS. The predictive value of computer-assisted semen analysis in the context of a donor insemination programme. Hum Reprod 1995; 10: 580–6.CrossRefGoogle ScholarPubMed
Feneux, D, Serres, C, Jouannet, P. Sliding spermatozoa: a dyskinesia responsible for human infertility? Fertil Steril 1985; 44: 508–11.CrossRefGoogle ScholarPubMed
Aitken, RJ, Sutton, M, Warner, P, Richardson, DW. Relationship between the movement characteristics of human spermatozoa and their ability to penetrate cervical mucus and zona-free hamster oocytes. J Reprod Fertil 1985; 73: 441–9.Google Scholar
Mortimer, D, Pandya, IJ, Sawers, RS. Relationship between human sperm motility characteristics and sperm penetration into human cervical mucus in vitro. J Reprod Fertil 1986; 78: 93–102.Google Scholar
Aitken, RJ, Warner, PE, Reid, C. Factors influencing the success of sperm-cervical mucus interaction in patients exhibiting unexplained infertility. J Androl 1986; 7: 3–10.CrossRefGoogle ScholarPubMed
Alasmari, W, Barratt, CLR, Publicover, SJ, et al. The clinical significance of calcium signaling pathways mediating human sperm hyperactivation. Hum Reprod 2013; 28: 866–76.Google Scholar
Sadeghi, S, García-Molina, A, Celma, F, et al. Morphometric comparison by the ISAS® CASA-DNAf system of two techniques for the evaluation of DNA fragmentation in human spermatozoa. Asian J Androl 2016; 18: 835–9.Google ScholarPubMed
Mortimer, D, Curtis, EF, Miller, RG. Specific labelling by peanut agglutinin of the outer acrosomal membrane of the human spermatozoon. J Reprod Fertil 1987; 81: 127–35.Google Scholar
Björndahl, L, Barratt, CLR, Mortimer, D, Jouannet, P. How to count sperm properly: checklist for acceptability of studies based on human semen analysis. Hum Reprod 2016; 31: 227–32.Google Scholar
Agarwal, A, Henkel, R, Huang, C-C, Lee, M-S. Automation of human semen analysis using a novel artificial intelligence optical microscopic technology. Andrologia 2019; 51: e13440.CrossRefGoogle ScholarPubMed
Dearing, CG, Kilburn, S, Lindsay, KS. Validation of the sperm class analyser CASA system for sperm counting in a busy diagnostic semen analysis laboratory. Hum Fertil 2014; 17: 37–44.CrossRefGoogle Scholar
Dearing, C, Jayasena, C, Lindsay, K. Can the Sperm Class Analyser (SCA) CASA-Mot system for human sperm motility analysis reduce imprecision and operator subjectivity and improve semen analysis? Hum Fertil 2021; 24: 208–18. https://doi.org/10.1080/14647273.2019.1610581Google Scholar
International Standards Organization. ISO 15189:2012 Medical laboratories – Requirements for quality and competence. Geneva: International Standards Organization, 2012.Google Scholar
Palacios, ER, Clavero, A, Gonzalvo, MC, et al. Acceptable variability in external quality assessment programmes for basic semen analysis. Hum Reprod 2012; 27: 314–22.CrossRefGoogle ScholarPubMed
Sanders, D, Fensome-Rimmer, S, Woodward, B. Uncertainty of measurement in andrology: UK best practice guideline from the Association of Biomedical Andrologists. Br J Biomed Sci 2017; 74: 157–62.CrossRefGoogle ScholarPubMed
International Standards Organization. ISO/TS 20914:2019 Medical laboratories – Practical guidance for the estimation of measurement uncertainty. Geneva: International Standards Organization, 2019.Google Scholar
Keel, BA, Quinn, P, Schmidt, CF Jr., et al. Results of the American Association of Bioanalysts national proficiency testing programme in andrology. Hum Reprod 2000; 15: 680–6.Google Scholar
Bailey, E, Fenning, N, Chamberlain, S, et al. Validation of sperm counting methods using limits of agreement J Androl 2007; 28: 364–73.Google Scholar
International Standards Organization. ISO 23162:2021 Basic semen examination – Specification and test methods. Geneva: International Standards Organization, 2021.Google Scholar
Bland, JM, Altman, DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986; I: 307–10.Google Scholar
Alquezar-Baeta, C, Gimeno-Martos, S, Miguel-Jimenez, S, et al. OpenCASA: a new open-source and scalable tool for sperm quality analysis. PLoS Comput Biol 2019; 15: e1006691.Google Scholar
Yániz, J, Alquézar-Baeta, C, Yagüe-Martínez, J, et al. Expanding the limits of computer-assisted sperm analysis through the development of open software. Biology (Basel) 2020; 9: 207.Google Scholar
Gallagher, MT, Cupples, G, Ooi, EH, et al. Rapid sperm capture: high-throughput flagellar waveform analysis. Hum Reprod 2019; 34: 1173–85.Google Scholar
Chu, KY, Nassau, DE, Arora, H. Artificial intelligence in reproductive urology. Curr Urol Rep 2019; 20: 52. https://doi.org/10.1007/s11934-019–0914–4Google Scholar
Riegler, M, et al. Artificial intelligence in the fertility clinic – status, pitfalls, and possibilities. Hum Reprod 2021; 36: 2429–42. https://doi.org/10.1093/humrep/deab168Google Scholar
Chang, V, Garcia, A, Hitschfeld, N, et al. Gold-standard for computer-assisted morphological sperm analysis. Comput Biol Med 2017; 83: 143–50.Google Scholar
Hicks, SA, Andersen, JM, Witczak, O, et al. Machine learning-based analysis of sperm videos and participant data for male fertility prediction. Sci Rep 2019; 9: 16770. https://doi.org/10.1038/s41598-019-53217-yGoogle Scholar
Movahed, RA, Mohammadi, E, Orooji, M. Automatic segmentation of sperm’s parts in microscopic images of human semen smears using concatenated learning approaches. Comput Biol Med 2019; 109: 242–53.Google Scholar
Abbasi, A, Miahi, E, Mirroshandel, SA. Effect of deep transfer and multi-task learning on sperm abnormality detection. Comput Biol Med 2021; 128: 104121. https://doi:10.1016/j.compbiomed.2020.104121CrossRefGoogle ScholarPubMed
Ilhan, HO, Serbes, G, Aydin, N, et al. A fully automated hybrid human sperm detection and classification system based on mobile-net and the performance comparison with conventional methods. Med Biol Engineer Comput 2020; 58: 103845.Google Scholar
Kobori, Y, Pfanner, P, Prins, GS, Niederberger, C. Novel device for male infertility screening with single-ball lens microscope and smartphone. Fertil Steril 2016; 106: 574–8.Google Scholar
Wei, SY, Chao, HH, Huang, HP, et al. A collective tracking method for preliminary sperm analysis. Biomed Eng Online 2019; 18: 112.Google Scholar
Ilhan, HO, Aydin, N. Smartphone based sperm counting – An alternative way to the visual assessment technique in sperm concentration analysis. Multimed Tools Appl 2020; 79: 6409–35.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×