Jouannet, P., Wang, C., Eustache, F., Kold-Jensen, T. & Auger, J. Semen quality and male reproductive health: The controversy about human sperm concentration decline. Apmis 109, S48–S61 (2001).
Basnet, P., Hansen, S. A., Olaussen, I. K., Hentemann, M. A. & Acharya, G. Changes in the semen quality among 5739 men seeking infertility treatment in Northern Norway over past 20 years (1993–2012). J. Reprod. Biotechnol. Fertil. 5, 2058915816633539 (2016).
Levine, H. et al. Temporal trends in sperm count: A systematic review and meta-regression analysis. Hum. Reprod. Update 23, 646–659 (2017).
Virtanen, H. E., Jørgensen, N. & Toppari, J. Semen quality in the 21st century. Nat. Rev. Urol. 14, 120 (2017).
Amann, R. P. & Waberski, D. Computer-assisted sperm analysis (CASA): Capabilities and potential developments. Theriogenology 81, 5-17.e13 (2014).
Zinaman, M. J., Uhler, M. L., Vertuno, E., Fisher, S. G. & Clegg, E. D. Evaluation of computer-assisted semen analysis (CASA) with IDENT stain to determine sperm concentration. J. Androl. 17, 288–292 (1996).
Daloglu, M. U. et al. Label-free 3D computational imaging of spermatozoon locomotion, head spin and flagellum beating over a large volume. Light Sci. Appl. 7, 17121–17121 (2018).
Ferrara, M. A. et al. Label-free imaging and biochemical characterization of bovine sperm cells. Biosensors 5, 141–157 (2015).
Henkel, R. Sperm preparation: State-of-the-art—physiological aspects and application of advanced sperm preparation methods. Asian J. Androl. 14, 260 (2012).
Nascimento, J., Botvinick, E. L., Shi, L. Z., Durrant, B. & Berns, M. W. Analysis of sperm motility using optical tweezers. J. Biomed. Opt. 11, 044001 (2006).
Ramalho-Santos, J. et al. Probing the structure and function of mammalian sperm using optical and fluorescence microscopy. Modern Res. Educ. Topics Microsc. 1, 394–402 (2007).
Moscatelli, N. et al. Single-cell-based evaluation of sperm progressive motility via fluorescent assessment of mitochondria membrane potential. Sci. Rep. 7, 1–10 (2017).
Kao, S.-H., Chao, H.-T. & Wei, Y.-H. Multiple deletions of mitochondrial DNA are associated with the decline of motility and fertility of human spermatozoa. Mol. Human Reprod. 4, 657–666 (1998).
Da Costa, R., Amaral, S., Redmann, K., Kliesch, S. & Schlatt, S. Spectral features of nuclear DNA in human sperm assessed by Raman microspectroscopy: Effects of UV-irradiation and hydration. PLoS ONE 13, e0207786 (2018).
Dubey, V. et al. Partially spatially coherent digital holographic microscopy and machine learning for quantitative analysis of human spermatozoa under oxidative stress condition. Sci. Rep. 9, 1–10 (2019).
Martini, A. C. et al. Effects of alcohol and cigarette consumption on human seminal quality. Fertil. Steril. 82, 374–377 (2004).
Di Caprio, G. et al. Holographic imaging of unlabelled sperm cells for semen analysis: A review. J. Biophotonics 8, 779–789 (2015).
Rivenson, Y. et al. PhaseStain: The digital staining of label-free quantitative phase microscopy images using deep learning. Light Sci. Appl. 8, 1–11 (2019).
Butola, A., Ahmad, A., Dubey, V., Senthilkumaran, P. & Mehta, D. S. Spectrally resolved laser interference microscopy. Laser Phys. Lett. 15, 075602 (2018).
Majeed, H., Nguyen, T. H., Kandel, M. E., Kajdacsy-Balla, A. & Popescu, G. Label-free quantitative evaluation of breast tissue using Spatial Light Interference Microscopy (SLIM). Sci. Rep. 8, 1–9 (2018).
Lee, M. et al. Label-free optical quantification of structural alterations in Alzheimer’s disease. Sci. Rep. 6, 31034 (2016).
Doblas, A. I., Sánchez-Ortiga, E., Martínez-Corral, M., Saavedra, G. & Garcia-Sucerquia, J. Accurate single-shot quantitative phase imaging of biological specimens with telecentric digital holographic microscopy. J. Biomed. Opt. 19, 046022 (2014).
Sridharan, S., Macias, V., Tangella, K., Kajdacsy-Balla, A. & Popescu, G. Prediction of prostate cancer recurrence using quantitative phase imaging. Sci. Rep. 5, 1–10 (2015).
Shan, M., Kandel, M. E. & Popescu, G. Refractive index variance of cells and tissues measured by quantitative phase imaging. Opt. Express 25, 1573–1581 (2017).
Kim, T. et al. White-light diffraction tomography of unlabelled live cells. Nat. Photonics 8, 256 (2014).
Nguyen, T. H., Kandel, M. E., Rubessa, M., Wheeler, M. B. & Popescu, G. Gradient light interference microscopy for 3D imaging of unlabeled specimens. Nat. Commun. 8, 1–9 (2017).
Daloglu, M. U. et al. 3D imaging of sex-sorted bovine spermatozoon locomotion, head spin and flagellum beating. Sci. Rep. 8, 1–9 (2018).
Park, Y., Depeursinge, C. & Popescu, G. Quantitative phase imaging in biomedicine. Nat. Photonics 12, 578–589 (2018).
Jo, Y. et al. Quantitative phase imaging and artificial intelligence: A review. IEEE J. Sel. Top. Quantum Electron. 25, 1–14 (2018).
Mirsky, S. K., Barnea, I., Levi, M., Greenspan, H. & Shaked, N. T. Automated analysis of individual sperm cells using stain-free interferometric phase microscopy and machine learning. Cytometry Part A 91, 893–900 (2017).
Butola, A. et al. Volumetric analysis of breast cancer tissues using machine learning and swept-source optical coherence tomography. Appl. Opt. 58, A135–A141 (2019).
Girshovitz, P. & Shaked, N. T. Generalized cell morphological parameters based on interferometric phase microscopy and their application to cell life cycle characterization. Biomed. Opt. Express 3, 1757–1773 (2012).
Butola, A. et al. Deep learning architecture LightOCT for diagnostic decision support using optical coherence tomography images of biological samples. arXiv preprint, arXiv:1812.02487 (2018).
Li, F. et al. Deep learning-based automated detection of retinal diseases using optical coherence tomography images. Biomed. Opt. Express 10, 6204–6226 (2019).
Fanous, M., Keikhosravi, A., Kajdacsy-Balla, A., Eliceiri, K. W. & Popescu, G. Quantitative phase imaging of stromal prognostic markers in pancreatic ductal adenocarcinoma. Biomed. Opt. Express 11, 1354–1364 (2020).
Falk, T. et al. U-Net: Deep learning for cell counting, detection, and morphometry. Nat. Methods 16, 67–70 (2019).
Wang, H. et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nat. Methods 16, 103–110 (2019).
Choi, G. et al. Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography. Opt. Express 27, 4927–4943 (2019).
Song, Y. et al. A deep learning based framework for accurate segmentation of cervical cytoplasm and nuclei. In 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2903–2906 (2014).
Ozaki, Y. et al. Label-free classification of cells based on supervised machine learning of subcellular structures. PLoS ONE 14, e0211347 (2019).
Chaveiro, A., Machado, L., Frijters, A., Engel, B. & Woelders, H. Improvement of parameters of freezing medium and freezing protocol for bull sperm using two osmotic supports. Theriogenology 65, 1875–1890 (2006).
Watson, P. F. The causes of reduced fertility with cryopreserved semen. Anim. Reprod. Sci. 60, 481–492 (2000).
Wongtawan, T., Saravia, F., Wallgren, M., Caballero, I. & Rodríguez-Martínez, H. Fertility after deep intra-uterine artificial insemination of concentrated low-volume boar semen doses. Theriogenology 65, 773–787 (2006).
Agarwal, A., Prabakaran, S. A. & Said, T. M. Prevention of oxidative stress injury to sperm. J. Androl. 26, 654–660 (2005).
Lemma, A. Effect of cryopreservation on sperm quality and fertility. Artif. Insemin. Farm Anim. 12, 191–216 (2011).
Ramírez-Reveco, A., Hernández, J. L. & Aros, P. Long-term storing of frozen semen at −196 °C does not affect the post-thaw sperm quality of bull semen. Cryopreserv. Eukaryot. 91 (2016).
Donnelly, E. T., McClure, N. & Lewis, S. E. Antioxidant supplementation in vitro does not improve human sperm motility. Fertil. Steril. 72, 484–495 (1999).
Ingólfsson, H. I. et al. Lipid organization of the plasma membrane. J. Am. Chem. Soc. 136, 14554–14559 (2014).
Kopeika, J., Thornhill, A. & Khalaf, Y. The effect of cryopreservation on the genome of gametes and embryos: Principles of cryobiology and critical appraisal of the evidence. Hum. Reprod. Update 21, 209–227 (2015).
Donnelly, E. T., Steele, E. K., McClure, N. & Lewis, S. E. Assessment of DNA integrity and morphology of ejaculated spermatozoa from fertile and infertile men before and after cryopreservation. Hum. Reprod. 16, 1191–1199 (2001).
Woolley, D. & Richardson, D. Ultrastructural injury to human spermatozoa after freezing and thawing. Reproduction 53, 389–394 (1978).
Ozkavukcu, S., Erdemli, E., Isik, A., Oztuna, D. & Karahuseyinoglu, S. Effects of cryopreservation on sperm parameters and ultrastructural morphology of human spermatozoa. J. Assist. Reprod. Genet. 25, 403–411 (2008).
Barthelemy, C. et al. Ultrastructural changes in membranes and acrosome of human sperm during cryopreservation. Arch. Androl. 25, 29–40 (1990).
O’connell, M., McClure, N. & Lewis, S. The effects of cryopreservation on sperm morphology, motility and mitochondrial function. Hum. Reprod. 17, 704–709 (2002).
Takeda, M., Ina, H. & Kobayashi, S. Fourier-transform method of fringe-pattern analysis for computer-based topography and interferometry. JOSA 72, 156–160 (1982).
Goldstein, R. M., Zebker, H. A. & Werner, C. L. Satellite radar interferometry: Two-dimensional phase unwrapping. Radio Sci 23, 713–720 (1988).
Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT press, Cambridge, 2016).
Iwai, H. et al. Quantitative phase imaging using actively stabilized phase-shifting low-coherence interferometry. Opt. Lett. 29, 2399–2401 (2004).
Organization, WH. World Health Statistics 2010 (World Health Organization, Geneva, 2010).