Automated analysis of individual sperm cells using stain-free interferometric phase microscopy and machine learning

Cytometry Part A

Volumn 91, Issue 9, 2017, Pages 893-900

  Mirsky, Simcha K ^a   Barnea, Itay ^a   Levi, Mattan ^a   Greenspan, Hayit ^a   Shaked, Natan T ^a  

^a TEL AVIV UNIVERSITY   (Israel)

Author keywords

holography; in vitro fertilization; interference microscopy; machine learning; spermatozoa; support vector machine

Indexed keywords

ALGORITHM; CYTOLOGY; HUMAN; IN VITRO FERTILIZATION; MACHINE LEARNING; MALE; MICROSCOPY; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC; SPERMATOZOON; STAINING; SUPPORT VECTOR MACHINE;

ALGORITHMS; FERTILIZATION IN VITRO; HUMANS; MACHINE LEARNING; MALE; MICROSCOPY; ROC CURVE; SPERMATOZOA; STAINING AND LABELING; SUPPORT VECTOR MACHINE;

EID: 85029661023     PISSN: 15524922     EISSN: 15524930     Source Type: Journal    
DOI: 10.1002/cyto.a.23189     Document Type: Article

References (24)

1. Brezina PR, Ning N, Mitchell E, Zacur HA, Baramki TA, Zhao Y. Recent advances in assisted reproductive technology. Curr Obstet Gynecol Rep 2012;1:166–173.
2. Barnes FL. Equipment and general technical aspects of micromanipulation of gametes and embryos. In: Gardner DK, Weissman A, Howles CM, Shoham Z, editors. Textbook of Assisted Reproductive Technologies: Laboratory and Clinical Perspectives, 3rd ed. London: Informa Healthcare; 2004. pp 163–170.
3. World Health Organization. WHO Laboratory Manual for the Examination and Processing of Human Semen, 5th ed. Geneva: WHO Press; 2010.
4. Mirsky SK, Barnea I, Shaked NT. Label-Free quantitative imaging of sperm for in vitro fertilization using interferometric phase microscopy. J Fertil In Vitro-IVF-Worldwide Reprod Med Genet Stem Cell Biol 2016;190:
5. Shaked NT, Satterwhite LL, Rinehart MT, Wax A. Quantitative analysis of biological cells using digital holographic microscopy. In: Rosen J, editor. Holography, Research and Technologies. Rijeka, Croatia: InTech; 2011. pp 219–237.
6. Haifler M, Girshovitz P, Band G, Dardikman G, Madjar I, Shaked N. Interferometric phase microscopy for label-free morphological evaluation of sperm cells. Fertil Steril 2015;104:43–47.
7. Di Caprio G, Ferrara MA, Miccio L, Merola F, Memmolo P, Ferraro P, Coppola G. Holographic imaging of unlabelled sperm cells for semen analysis: A review. J Biophotonics 2014;8:779–789.
8. Chen S, Ryu J, Lee K, Zhu Y. Swept source digital holographic phase microscopy. Opt Lett 2016;41:665–668.
9. Balberg M, Levi M, Kalinowski K, Barnea I, Mirsky SK, Shaked NT. Localized measurements of physical parameters within human sperm cells obtained with wide-field interferometry. J Biophotonics (in press); DOI: 10.1002/jbio.201600186.
10. Javidi B, Moon I, Yeom S, Carapezza E. Three-dimensional imaging and recognition of microorganism using single-exposure on-line (SEOL) digital holography. Opt Express 2005;13:4492–4506.
11. Feizi A, Zhang Y, Greenbaum A, Guziak A, Luong M, Chan RYL, Berg B, Ozkan H, Luo W, Wu M, et al. Rapid, portable and cost-effective yeast cell viability and concentration analysis using lensfree on-chip microscopy and machine learning. Lab Chip 2016;16:4350–4358.
12. Park HS, Rinehart MT, Walzer KA, Chi JA, Wax A. Automated detection of P. falciparum using machine learning algorithms with quantitative phase images of unstained cells. PLoS One 2016;11:e0163045.
13. Roitshtain D, Wolbromsky L, Bal E, Greenspan H, Satterwhite L, Shaked NT. Quantitative phase microscopy spatial signatures of cancer cells. Cytometry Part A 2017;91A:482–493.
14. Robertson SD, Davidson WM. Symposium on Nuclear Sex. London: William Heinemann Medical Books Ltd.; 1958.
15. Girshovitz P, Shaked NT. Compact and portable low-coherence interferometer with off-axis geometry for quantitative phase microscopy and nanoscopy. Opt Express 2013;21:5701–5714.
16. Navarro MA, Estrada JC, Servin M, Quiroga JA, Vargas J. Fast two-dimensional simultaneous phase unwrapping and low-pass filtering. Opt Express 2012;20:2556–2561.
17. Menkveld R, Wong W, Lombard C, Wetzels A, Thomas C, Merkus H, Steegers TR. Semen parameters, including WHO and strict criteria morphology, in a fertile and subfertile population: An effort towards standardization of in-vivo thresholds. Hum Reprod 2001;16:1165–1171.
18. Berndt B. Ramanujan's Notebooks. New York: Springer-Verlag New York Inc.; 1985.
19. Chan TF, Vese LA. Active contours without edges. IEEE Trans Image Process 2001;10:266–277.
20. Carpenter A, Jones T, Lamprecht M, Clarke C, Kang I, Friman O, Guertin D, Chang J, Lindquist R, Moffat J, et al. CellProfiler: Image analysis software for identifying and quantifying cell phenotypes. Genome Biol 2006;7:R100.
21. Hall M. Correlation-Based Feature Subset Selection for Machine Learning. PhD Thesis, University of Waikato, Hamilton, New Zealand; 1998.
22. Eibe F, Hall MA, Witten IH, Pal JC. The WEKA workbench. In: Pitts T, editor. Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques, 4th ed. Cambridge, Massachusetts USA: Morgan Kaufmann Publishers; 2016.
23. Chang C, Lin C. LIBSVM: A library for support vector machines. ACM Trans Intell Syst Technol 2011;2.
24. Van Steirteghem A, Nagy Z, Liu J, Joris H, Verheyen G, Smitz J, Tournaye H, Liebaers I, Devroey P. Intracytoplasmic sperm injection. Baillières Clin Obstet Gynaecol 1994;8:1–208.