• Chinese Optics Letters
  • Vol. 19, Issue 5, 051701 (2021)
Ying Li1, Jianglei Di1、*, Li Ren2, and Jianlin Zhao1、**
Author Affiliations
  • 1MOE Key Laboratory of Material Physics and Chemistry under Extraordinary Conditions, and Shaanxi Key Laboratory of Optical Information Technology, School of Physical Science and Technology, Northwestern Polytechnical University, Xi’an 710129, China
  • 2School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China
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    DOI: 10.3788/COL202119.051701 Cite this Article Set citation alerts
    Ying Li, Jianglei Di, Li Ren, Jianlin Zhao. Deep-learning-based prediction of living cells mitosis via quantitative phase microscopy[J]. Chinese Optics Letters, 2021, 19(5): 051701 Copy Citation Text show less
    References

    [1] E. Zlotek-Zlotkiewicz, S. Monnier, G. Cappello, M. Le Berre, M. Piel. Optical volume and mass measurements show that mammalian cells swell during mitosis. J. Cell Biol., 211, 765(2015).

    [2] Y. T. Su, Y. Lu, M. Chen, A. A. Liu. Spatiotemporal joint mitosis detection using CNN-LSTM network in time-lapse phase contrast microscopy images. IEEE Access, 5, 18033(2017).

    [3] Y. Zhou, H. Mao, Z. Yi. Cell mitosis detection using deep neural networks. Knowledge-Based Syst., 137, 19(2017).

    [4] W. Nie, Y. Yan, T. Hao, C. Liu, Y. Su. Mitosis event recognition and detection based on evolution of feature in time domain. Mach. Vis. Appl., 29, 1249(2018).

    [5] Ş. Öztürk, B. Akdemir. A convolutional neural network model for semantic segmentation of mitotic events in microscopy images. Neural Comput. Appl., 31, 3719(2018).

    [6] G. Jimenez, D. Racoceanu. Deep learning for semantic segmentation vs. classification in computational pathology: application to mitosis analysis in breast cancer grading. Front. Bioeng. Biotech., 7, 145(2019).

    [7] P. Eulenberg, N. Kohler, T. Blasi, A. Filby, A. E. Carpenter, P. Rees, F. J. Theis, F. A. Wolf. Reconstructing cell cycle and disease progression using deep learning. Nat. Commun., 8, 463(2017).

    [8] Y. Park, C. Depeursinge, G. Popescu. Quantitative phase imaging in biomedicine. Nat. Photon., 12, 578(2018).

    [9] H. Majeed, S. Sridharan, M. Mir, L. Ma, E. Min, W. Jung, G. Popescu. Quantitative phase imaging for medical diagnosis. J. Biophoton., 10, 177(2017).

    [10] Y. Jo, H. Cho, S. Y. Lee, G. Choi, G. Kim, H. S. Min, Y. Park. Quantitative phase imaging and artificial intelligence: a review. IEEE J. Sel. Top. Quantum Electron, 25, 6800914(2019).

    [11] J. Cheng, J. Luo. Tikhonov-regularization-based projecting sparsity pursuit method for fluorescence molecular tomography reconstruction. Chin. Opt. Lett., 18, 011701(2020).

    [12] S. Deng, Y. Xiao, J. Hu, J. Chen, Y. Wang, M. Liu. Sidelobe suppression in light-sheet fluorescence microscopy with Bessel beam plane illumination using subtractive imaging. Chin. Opt. Lett., 16, 111801(2018).

    [13] A. K. Bryan, A. Goranov, A. Amon, S. R. Manalis. Measurement of mass, density, and volume during the cell cycle of yeast. Proc. Natl. Acad. Sci. USA, 107, 999(2010).

    [14] P. Girshovitz, N. T. Shaked. Generalized cell morphological parameters based on interferometric phase microscopy and their application to cell life cycle characterization. Biomed. Opt. Express, 3, 1757(2012).

    [15] P. Fei, J. Nie, J. Lee, Y. Ding, S. Li, H. Zhang, M. Hagiwara, T. Yu, T. Segura, C.-M. Ho, D. Zhu, T. K. Hsiai. Subvoxel light-sheet microscopy for high-resolution high-throughput volumetric imaging of large biomedical specimens. Adv. Photon., 1, 016002(2019).

    [16] S. H. Karandikar, C. Zhang, A. Meiyappan, I. Barman, C. Finck, P. K. Srivastava, R. Pandey. Reagent-free and rapid assessment of T cell activation state using diffraction phase microscopy and deep learning. Anal. Chem., 91, 3405(2019).

    [17] L. Zheng, K. Yu, S. Cai, Y. Wang, B. Zeng, M. Xu. Lung cancer diagnosis with quantitative DIC microscopy and a deep convolutional neural network. Biomed. Opt. Express, 10, 2446(2019).

    [18] Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. H. Kim, S. J. Kang, M. C. Choi, S. Y. Lee, Y. Park. Holographic deep learning for rapid optical screening of anthrax spores. Sci. Adv., 3, e1700606(2017).

    [19] Y. Li, J. Di, C. Ma, J. Zhang, J. Zhong, K. Wang, T. Xi, J. Zhao. Quantitative phase microscopy for cellular dynamics based on transport of intensity equation. Opt. Express, 26, 586(2018).

    [20] C. Zuo, Q. Chen, W. Qu, A. Asundi. Noninterferometric single-shot quantitative phase microscopy. Opt. Lett., 38, 3538(2013).

    [21] X. Tian, W. Yu, X. Meng, A. Sun, L. Xue, C. Liu, S. Wang. Real-time quantitative phase imaging based on transport of intensity equation with dual simultaneously recorded field of view. Opt. Lett., 41, 1427(2016).

    [22] T. E. Gureyev, K. A. Nugent. Phase retrieval with the transport-of-intensity equation. II. Orthogonal series solution for nonuniform illumination. J. Opt. Soc. Am. A, 13, 1670(1996).

    [23] M. Beleggia, M. A. Schofield, V. V. Volkov, Y. Zhu. On the transport of intensity technique for phase retrieval. Ultmi, 102, 37(2004).

    [24] J. M. Wittkopp, T. C. Khoo, S. Carney, K. Pisila, S. J. Bahreini, K. Tubbesing, S. Mahajan, A. Sharikova, J. C. Petruccelli, A. Khmaladze. Comparative phase imaging of live cells by digital holographic microscopy and transport of intensity equation methods. Opt. Express, 28, 6123(2020).

    [25] T. Chakraborty, J. C. Petruccelli. Source diversity for transport of intensity phase imaging. Opt. Express, 25, 9122(2017).

    [26] C. J. Sheppard. Defocused transfer function for a partially coherent microscope and application to phase retrieval. J. Opt. Soc. Am. A, 21, 828(2004).

    [27] A. Krizhevsky, I. Sutskever, G. E. HintonInternational Conference on Neural Information Processing Systems. ImageNet classification with deep convolutional neural networks, 1097(2012).

    [28] Y. Li, J. Di, K. Wang, S. Wang, J. Zhao. Classification of cell morphology with quantitative phase microscopy and machine learning. Opt. Express, 28, 23916(2020).

    [29] L. Strbkova, D. Zicha, P. Vesely, R. Chmelik. Automated classification of cell morphology by coherence-controlled holographic microscopy. J. Biomed. Opt., 22, 086008(2017).

    Data from CrossRef

    [1] Yuanyuan Xu, Shuangshuang Xue, Yang Zou, Jingrong Liao, Yujuan Sun, Yawei Wang. Fast classification and recognition method of blood cells using deep learning based on wrapped phase in polar coordinate. Optik, 169175(2022).

    Ying Li, Jianglei Di, Li Ren, Jianlin Zhao. Deep-learning-based prediction of living cells mitosis via quantitative phase microscopy[J]. Chinese Optics Letters, 2021, 19(5): 051701
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