NIST online presentation: Prof. Gabriel Popescu on Phase Imaging with Computational Specificity (PICS) – May 26

Phase Imaging with Computational Specificity (PICS)
Live digital staining of unlabeled (no fluorescent stains) cell cultures using Phase Imaging with Computational Specificity (PICS) (courtesy UIUC Beckman QLI lab)

NIST MML and ITL groups are hosting an open presentation by Prof. Gabriel Popescu (UIUC Beckman) on “Phase Imaging with Computational Specificity (PICS) for biomedical applications” on Thursday, May 26 2022.


Quantitative phase imaging (QPI) has gained significant interest, especially in the past decade, because of its ability to study unlabeled cells and tissues. As a result, QPI can extract structure and dynamics information from live cells without photodamage or photobleaching. However, in the absence of labels, QPI cannot identify easily particular structures in the cell, i.e., it lacks specificity. This represents the major limitation of QPI when applied in biomedicine. 
Recently, deep learning techniques have been translating from consumer to scientific applications. For example, it has been shown that AI can map one form of contrast into another. Significantly, it has been demonstrated that the neural network can learn from label-free (bright field, phase contrast, DIC) and ground-truth fluorescence images to predict where specific fluorophores would bind in an unlabeled specimen. 
Inspired by this prior work, we applied deep learning to QPI data, generated by SLIM and GLIM. These methods are white-light and common-path and, thus, provide high spatial and temporal sensitivity. Because they are add-on to existing microscopes and compatible with the fluorescence channels, these methods provide simultaneous phase and fluorescence from the same field of view. As a result, the training data necessary for deep learning is generated automatically.  
We present a new microscopy concept, where the process of retrieving computational specificity is part of the acquisition software, performed in real-time. We demonstrate this idea with various fluorescence tags and operation on live cells as well as tissue pathology. This new type of microscopy can potentially replace some commonly used tags and stains and eliminate the inconveniences associated with phototoxicity and photobleaching. Phase imaging with computational specificity (PICS) has an enormous potential for biomedicine.


Gabriel Popescu is the William L. Everitt Distinguished Professor in Electrical and Computer Engineering, University of Illinois at Urbana-Champaign. He received his Ph.D. in Optics from the School of Optics/ CREOL (now the College of Optics and Photonics), University of Central Florida. He continued his training with the late Michael Feld at M.I.T., working as a postdoctoral associate. He joined Illinois in August 2007 where he directs the Quantitative Light Imaging Laboratory (QLI Lab) at the Beckman Institute for Advanced Science and Technology. He served as Associate Editor of Optics Express and Biomedical Optics Express, Editorial Board Member for Journal of Biomedical Optics and Scientific Reports. He founded Phi Optics, Inc., a start-up company that commercializes quantitative phase imaging technology. He is a Fellow of OSA, SPIE, AIMBE, and Senior member of IEEE

Selected references (more at QLI lab publications)

  1. Hu, C., S. He, Y. J. Lee, Y. He, E. M. Kong, H. Li, M. A. Anastasio and G. Popescu (2022). “Live-dead assay on unlabeled cells using phase imaging with computational specificity.” Nature Communications 13(1): 713.
  2. Goswami, N., Y. R. He, Y.-H. Deng, C. Oh, N. Sobh, E. Valera, R. Bashir, N. Ismail, H. Kong, T. H. Nguyen, C. Best-Popescu and G. Popescu (2021). “Label-free SARS-CoV-2 detection and classification using phase imaging with computational specificity.” Light: Science & Applications 10(1): 176.
  3. Kandel, M. E., M. Rubessa, Y. R. He, S. Schreiber, S. Meyers, L. Matter Naves, M. K. Sermersheim, G. S. Sell, M. J. Szewczyk, N. Sobh, M. B. Wheeler and G. Popescu (2020). “Reproductive outcomes predicted by phase imaging with computational specificity of spermatozoon ultrastructure.” Proceedings of the National Academy of Sciences 117(31): 18302-18309.
  4. Kandel, M. E., Y. R. He, Y. J. Lee, T. H.-Y. Chen, K. M. Sullivan, O. Aydin, M. T. A. Saif, H. Kong, N. Sobh and G. Popescu (2020). “Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments.” Nature Communications 11(1): 6256.
  5. Hu, C., J. J. Field, V. Kelkar, B. Chiang, K. Wernsing, K. C. Toussaint, R. A. Bartels and G. Popescu (2020). “Harmonic optical tomography of nonlinear structures.” Nature Photonics.
  6. Kandel, M. E., C. Hu, G. Naseri Kouzehgarani, E. Min, K. M. Sullivan, H. Kong, J. M. Li, D. N. Robson, M. U. Gillette, C. Best-Popescu and G. Popescu (2019). “Epi-illumination gradient light interference microscopy for imaging opaque structures.” Nature Communications 10(1): 4691.
  7. Park, Y., C. Depeursinge and G. Popescu (2018). “Quantitative phase imaging in biomedicine.” Nature Photonics 12(10): 578.
  8. Nguyen, T. H., M. E. Kandel, M. Rubessa, M. B. Wheeler and G. Popescu (2017). “Gradient light interference microscopy for 3D imaging of unlabeled specimens.” Nat Commun 8(1): 210.
  9. Kim, T., R. J. Zhou, M. Mir, S. D. Babacan, P. S. Carney, L. L. Goddard and G. Popescu (2014). “White-light diffraction tomography of unlabeled live cells.” Nature Photonics 8(3): 256-263.
  10. Popescu, G. (2011). Quantitative phase imaging of cells and tissues. New York, McGraw-Hill.

Reach out for the link to the live webinar (before May 26, 2022)

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