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Morphometric evaluation of two-pronucleus zygote images using image-processing techniques

Published online by Cambridge University Press:  17 August 2022

Niloofar Sayadi ,
Sara Monji-Azad ,
Seyed Abolghasem Mirroshandel Open the ORCID record for Seyed Abolghasem Mirroshandel [Opens in a new window]  and
Fatemeh Ghasemian
Niloofar Sayadi
Affiliation:
Department of Computer Engineering, University of Guilan, Rasht, Iran
Sara Monji-Azad
Affiliation:
Mannheim Institute for Intelligent Systems in Medicine, Heidelberg University, Heidelberg, Germany
Seyed Abolghasem Mirroshandel*
Affiliation:
Department of Computer Engineering, University of Guilan, Rasht, Iran
Fatemeh Ghasemian
Affiliation:
Department of Biology, University of Guilan, Rasht, Iran
*
Author for correspondence: S.A. Mirroshandel. Department of Computer Engineering, University of Guilan, Rasht, P.O. Box: 1841, Iran. Tel.: +98 13 33690274. Fax: +98 13 33690271. E-mail: mirroshandel@guilan.ac.ir
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Summary

Identifying embryos with a high potential for implementation remains a challenge in in vitro fertilization (IVF) cycles. Despite progress in IVF treatment, only a minority of generated embryos has the ability to implant. Another drawback of this practice is the high frequency of multiple pregnancies. This problem leads to economic and health problems. Therefore, the transfer of a single embryo with high implantation potential is the ideal strategy. Morphometric evaluation of two-pronucleus zygote images is a helpful technique when aiming to transfer a single embryo with a high implantation potential. In this study, an automated zygote morphometric evaluation algorithm, called the zygote morphology evaluation (ZME) algorithm, was created to analyze the zygote and provide morphological measurements. The first and most crucial step of the ZME algorithm is the noise reduction step, which was first applied to zygote images. After that, the proposed algorithm detects different parts of the zygote that are indicators of embryo viability and normality, that is the oolemma, perivitelline space, zona pellucida, and nucleolar precursor bodies (NPBs). In addition, a novel dataset was prepared for this task. This dataset consisted of 703 human zygote images, and called the human zygote morphometric evaluation dataset (HZME-DS). Our experimental results in the HZME-DS showed that the ZME algorithm was able to achieve 79.58% average accuracy in identifying the oolemma region, 79.40% average accuracy in determining the perivitelline space, and 79.72% accuracy in identifying the zona pellucida. To calculate the accuracy of identifying NPBs, the proposed algorithm uses Recall and Precision measures, and their harmonic average (F1 measure) reached values of 81.14% and 79.53%, respectively. These encouraging results for our proposed method, which is an automatic and very fast method, showed that the ZME algorithm could help embryologists to evaluate the best zygotes in real time and the best embryos subsequently.

Keywords

In vitro fertilizationImage processingMorphometric evaluationPronucleus morphologyZygote
Type
Research Article
Information
Zygote , Volume 30 , Issue 6 , December 2022 , pp. 819 - 829
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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References

Awcock, G. J. and Thomas, R. (1995). Applied image processing. Macmillan International Higher Education.CrossRefGoogle Scholar
Bączkowski, T., Kurzawa, R. and Głąbowski, W. (2004). Methods of embryo scoring in in vitro fertilization. Reproductive Biology, 4(1), 5–22.Google ScholarPubMed
Basile, T. M. A., Caponetti, L., Castellano, G. and Sforza, G. (2010). A texture-based image processing approach for the description of human oocyte cytoplasm. IEEE Transactions on Instrumentation and Measurement, 59(10), 2591–2601. doi: 10.1109/TIM.2010.2057552 CrossRefGoogle Scholar
Beuchat, A., Thévenaz, P., Unser, M., Ebner, T., Senn, A., Urner, F., Germond, M. and Sorzano, C. O. (2008). Quantitative morphometrical characterization of human pronuclear zygotes. Human Reproduction, 23(9), 1983–1992. doi: 10.1093/humrep/den206 CrossRefGoogle ScholarPubMed
Bezdek, J. C. (2013). Pattern recognition with fuzzy objective function algorithms. Springer Science+Business Media.Google Scholar
Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679–698. doi: 10.1109/TPAMI.1986.4767851 CrossRefGoogle ScholarPubMed
El-Shenawy, M. (2013). Automatic detection and identification of cells in digital images of day 2 IVF embryos. University of Salford.Google Scholar
Ghasemian, F., Mirroshandel, S. A., Monji-Azad, S., Azarnia, M. and Zahiri, Z. (2015). An efficient method for automatic morphological abnormality detection from human sperm images. Computer Methods and Programs in Biomedicine, 122(3), 409–420. doi: 10.1016/j.cmpb.2015.08.013 CrossRefGoogle ScholarPubMed
Giusti, A., Corani, G., Gambardella, L., Magli, C. and Gianaroli, L. (2009). Segmentation of human zygotes in Hoffman modulation contrast images. Proceedings of the of MIUA.Google Scholar
Gonzalez, R. C., Woods, R. E. and Eddins, S. L. (2004). Digital image processing using MATLAB. Pearson Education.Google Scholar
Han, J., Pei, J. and Kamber, M. (2011). Data mining: Concepts and techniques. Elsevier.Google Scholar
Kass, M., Witkin, A. and Terzopoulos, D. (1988). Snakes: Active contour models. International Journal of Computer Vision, 1(4), 321–331. doi: 10.1007/BF00133570 CrossRefGoogle Scholar
Kuijper, A. and Heise, B. (2008). An automatic cell segmentation method for differential interference contrast microscopy. In “2008 19th International Conference on Pattern Recognition”, IEEE (pp. 1–4).CrossRefGoogle Scholar
Louis, C. M., Erwin, A., Handayani, N., Polim, A. A., Boediono, A. and Sini, I. (2021). Review of computer vision application in in vitro fertilization: The application of deep learning-based computer vision technology in the world of IVF. Journal of Assisted Reproduction and Genetics, 38(7), 1627–1639. doi: 10.1007/s10815-021-02123-2 CrossRefGoogle ScholarPubMed
Mölder, A., Czanner, S., Costen, N. and Hartshorne, G. (2014). Automatic detection of embryo location in medical imaging using trigonometric rotation for noise reduction. In: 2014 22nd International Conference on Pattern Recognition (pp. 3239–3244). IEEE.Google Scholar
Morales, D. A., Bengoetxea, E. and Larranaga, P. (2008). Automatic segmentation of zona pellucida in human embryo images applying an active contour model. Proceedings of the of MIUA.Google Scholar
Powers, D. M. (2011). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation.Google Scholar
Prasad, J. R., Kulkarni, U. V. and Prasad, R. S. (2009). Template matching algorithm for Gujrati character recognition. In “2009 Second International Conference on Emerging Trends in Engineering & Technology” (pp. 263–268). IEEE Publications.CrossRefGoogle Scholar
Puissant, F., Van Rysselberge, M., Barlow, P., Deweze, J. and Leroy, F. (1987). Embryo scoring as a prognostic tool in IVF treatment. Human Reproduction, 2(8), 705–708. doi: 10.1093/oxfordjournals.humrep.a136618 CrossRefGoogle ScholarPubMed
Rodellar, J., Alférez, S., Acevedo, A., Molina, A. and Merino, A. (2018). Image processing and machine learning in the morphological analysis of blood cells. International Journal of Laboratory Hematology, 40 Suppl. 1, 46–53. doi: 10.1111/ijlh.12818 CrossRefGoogle ScholarPubMed
Santos Filho, E. S., Noble, J. A., Poli, M., Griffiths, T., Emerson, G. and Wells, D. (2012). A method for semi-automatic grading of human blastocyst microscope images. Human Reproduction, 27(9), 2641–2648. doi: 10.1093/humrep/des219 CrossRefGoogle ScholarPubMed
Scott, L., Alvero, R., Leondires, M. and Miller, B. (2000). The morphology of human pronuclear embryos is positively related to blastocyst development and implantation. Human Reproduction, 15(11), 2394–2403. doi: 10.1093/humrep/15.11.2394 CrossRefGoogle ScholarPubMed
Shapiro, L. G. and Stockman, G. C. (2001). Computer vision. Prentice Hall.Google Scholar
Strouthopoulos, C. and Anifandis, G. (2018). An automated blastomere identification method for the evaluation of day 2 embryos during IVF/ICSI treatments. Computer Methods and Programs in Biomedicine, 156, 53–59. doi: 10.1016/j.cmpb.2017.12.022 CrossRefGoogle ScholarPubMed
VerMilyea, M., Hall, J. M. M., Diakiw, S. M., Johnston, A., Nguyen, T., Perugini, D., Miller, A., Picou, A., Murphy, A. P. and Perugini, M. (2020). Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF. Human Reproduction, 35(4), 770–784. doi: 10.1093/humrep/deaa013 CrossRefGoogle ScholarPubMed
Wang, Z., Ang, W. T., Tan, S. Y. M. and Latt, W. T. (2015). Automatic segmentation of zona pellucida and its application in cleavage-stage embryo biopsy position selection. In. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. IEEE Publications, 2015, 3859–3864. doi: 10.1109/EMBC.2015.7319236 Google Scholar
Yang, F. and Jiang, T. (2001). Cell image segmentation with kernel-based dynamic clustering and an ellipsoidal cell shape model. Journal of Biomedical Informatics, 34(2), 67–73. doi: 10.1006/jbin.2001.1009 CrossRefGoogle Scholar
Zhao, M., Li, H., Li, R., Li, Y., Luo, X., Li, T. C., Lee, T. L., Wang, W. J. and Chan, D. Y. L. (2021). Automated and precise recognition of human zygote cytoplasm: A robust image-segmentation system based on a convolutional neural network. Biomedical Signal Processing and Control, 67, 102551. doi: 10.1016/j.bspc.2021.102551 CrossRefGoogle Scholar
Ziebe, S., Petersen, K., Lindenberg, S., Andersen, A. G., Gabrielsen, A. and Andersen, A. N. (1997). Embryo morphology or cleavage stage: How to select the best embryos for transfer after in-vitro fertilization. Human Reproduction, 12(7), 1545–1549. doi: 10.1093/humrep/12.7.1545 CrossRefGoogle ScholarPubMed