Abstract
Autofocusing is essential and serves as a prerequisite for the automation of embryo biopsy in preimplantation genetic testing (PGT) using robotics. Despite the existence of numerous autofocus algorithms, achieving accurate autofocusing for cleavage-stage embryos in brightfield microscopy remains challenging due to their non-unimodal nature. Thus, an adaptive and robust autofocusing method is required. This paper presents an autofocusing method based on an adaptive focus measure. The proposed method employs an adaptive focus measure selection approach and incorporates a coarse-to-fine strategy for autofocusing cleavage-stage embryos in brightfield microscopy. Experimental results demonstrate that the proposed autofocusing method achieves superior performance compared to other regular autofocus algorithms in brightfield microscopy for cleavage-stage embryos. This advancement marks a significant step forward in the automation development of PGT.
S. Yao and K. Wu—These authors contributed equally to this work.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Brezina, P.R., Brezina, D.S., Kearns, W.G.: Preimplantation genetic testing. BMJ 345, e5908 (2012). https://doi.org/10.1136/bmj.e5908
Kang, H.-J., Melnick, A.P., Stewart, J.D., Xu, K., Rosenwaks, Z.: Preimplantation genetic screening: who benefits? Fertil. Steril. 106(3), 597–602 (2016). https://doi.org/10.1016/j.fertnstert.2016.04.027
Won, S.Y., Kim, H., Lee, W.S., Kim, J.W., Shim, S.H.: Pre-implantation genetic diagnosis and pre-implantation genetic screening: two years experience at a single center. Obstet. Gynecol. Sci. 61(1), 95–101 (2018). https://doi.org/10.5468/ogs.2018.61.1.95
Liu, X., Lu, Z., Sun, Y.: Orientation control of biological cells under inverted microscopy. IEEE/ASME Trans. Mechatron. 16(5), 918–924 (2011). https://doi.org/10.1109/TMECH.2010.2056380
Zhao, X., Cui, M., Zhang, Y., Liu, Y., Zhao, X.: Robotic precisely oocyte blind enucleation method. Appl. Sci. 11(4), 1850 (2021). https://doi.org/10.3390/app11041850
Wei, Y., Xu, Q.: A survey of force-assisted robotic cell microinjection technologies. IEEE Trans. Autom. Sci. Eng. 16(2), 931–945 (2019). https://doi.org/10.1109/TASE.2018.2878867
Shan, G., et al.: 3D morphology measurement for blastocyst evaluation from ‘all angles’. IEEE Trans. Biomed. Eng. 1–10 (2022). https://doi.org/10.1109/TBME.2022.3232068
Yao, S., Li, H., Pang, S., Zhu, B., Zhang, X., Fatikow, S.: A review of computer microvision-based precision motion measurement: principles, characteristics, and applications. IEEE Trans. Instrum. Meas. 70, 1–28 (2021). https://doi.org/10.1109/TIM.2021.3065436
Zhang, Z., Wang, X., Liu, J., Dai, C., Sun, Y.: Robotic micromanipulation: fundamentals and applications. Annu. Rev. Control Robot. Auton. Syst. 2(1), 181–203 (2019). https://doi.org/10.1146/annurev-control-053018-023755
Sun, Y., Duthaler, S., Nelson, B.J.: Autofocusing in computer microscopy: selecting the optimal focus algorithm. Microsc. Res. Tech. 65(3), 139–149 (2004). https://doi.org/10.1002/jemt.20118
Yu, M.Y., Han, M.L., Shee, C.Y., Ang, W.T.: Autofocusing algorithm comparison in bright field microscopy for automatic vision aided cell micromanipulation. In: 2010 IEEE International Conference on Nano/Molecular Medicine and Engineering, Hung Hom, China, pp. 88–92. IEEE (2010). https://doi.org/10.1109/NANOMED.2010.5749811
Pertuz, S., Puig, D., Garcia, M.A.: Analysis of focus measure operators for shape-from-focus. Pattern Recogn. 46(5), 1415–1432 (2013). https://doi.org/10.1016/j.patcog.2012.11.011
Su, L., et al.: Macro-to-micro positioning and auto focusing for fully automated single cell microinjection. Microsyst. Technol. 27(1), 11–21 (2021). https://doi.org/10.1007/s00542-020-04891-w
Wang, Z., Feng, C., Ang, W.T., Tan, S.Y.M., Latt, W.T.: Autofocusing and polar body detection in automated cell manipulation. IEEE Trans. Biomed. Eng. 64(5), 1099–1105 (2017). https://doi.org/10.1109/TBME.2016.2590995
Luo, Y., Huang, L., Rivenson, Y., Ozcan, A.: Single-shot autofocusing of microscopy images using deep learning. ACS Photon. 8(2), 625–638 (2021). https://doi.org/10.1021/acsphotonics.0c01774
Yao, S., Mills, J.K., Ajamieh, I.A., Li, H., Zhang, X.M.: Automatic three-dimensional imaging for blastomere identification in early-stage embryos based on brightfield microscopy. Opt. Lasers Eng. 130, 106093 (2020). https://doi.org/10.1016/j.optlaseng.2020.106093
Acknowledgments
This work was supported in part by Science and Technology Program of Guangzhou under Grant 2023A04J2039 and National Training Program of Innovation and Entrepreneurship for Undergraduates under Grant 202212121051.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yao, S., Wu, K., Qi, L., Yang, F., Feng, Q. (2023). Autofocusing for Cleavage-stage Embryos in Brightfield Microscopy: Towards Automated Preimplantation Genetic Testing. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14269. Springer, Singapore. https://doi.org/10.1007/978-981-99-6489-5_15
Download citation
DOI: https://doi.org/10.1007/978-981-99-6489-5_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-6488-8
Online ISBN: 978-981-99-6489-5
eBook Packages: Computer ScienceComputer Science (R0)