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Artificial intelligence-enabled quantitative phase imaging methods for life sciences

Nature Methods volume 20, pages 1645–1660 (2023)Cite this article

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Abstract

Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and label-free investigation of the physiology and pathology of biological systems. This review presents the principles of various two-dimensional and three-dimensional label-free phase imaging techniques that exploit refractive index as an intrinsic optical imaging contrast. In particular, we discuss artificial intelligence-based analysis methodologies for biomedical studies including image enhancement, segmentation of cellular or subcellular structures, classification of types of biological samples and image translation to furnish subcellular and histochemical information from label-free phase images. We also discuss the advantages and challenges of artificial intelligence-enabled quantitative phase imaging analyses, summarize recent notable applications in the life sciences, and cover the potential of this field for basic and industrial research in the life sciences.

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Fig. 1: AI-integrated QPI methods applied to biomedical research.
Fig. 2: Principles and advantages of QPI.
Fig. 3: Image reconstruction and enhancement of QPI using AI.
Fig. 4: Using deep learning for organelle segmentation in QPI images of biological cells.
Fig. 5: Diverse classification applications in life science using QPI and AI.
Fig. 6: AI-enabled image translation from QPI images to other imaging modalities for various biological systems.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (2015R1A3A2066550, 2022M3H4A1A02074314, RS-2023-00241278), an Institute of Information & Communications Technology Planning & Evaluation (IITP; 2021-0-00745) grant funded by the Korea government (MSIT), a KAIST Institute of Technology Value Creation, Industry Liaison Center (G-CORE Project) grant funded by MSIT (N11230131), the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Korea (HI21C0977, HR22C1605), the US National Science Foundation (NSF) Biophotonics Program, NSF PATHS-UP Engineering Research Center and Koç Group.

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Author notes

  1. DongHun Ryu

    Present address: Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA

  2. These authors contributed equally: Juyeon Park, Bijie Bai.

Authors and Affiliations

  1. Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea

    Juyeon Park, DongHun Ryu, Chungha Lee, Geon Kim & YongKeun Park

  2. KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea

    Juyeon Park, DongHun Ryu, Chungha Lee, Mahn Jae Lee, Jeongwon Shin, Geon Kim & YongKeun Park

  3. Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA

    Bijie Bai, Tairan Liu, Yi Luo, Luzhe Huang, Yijie Zhang, Yuzhu Li & Aydogan Ozcan

  4. Bioengineering Department, University of California, Los Angeles, Los Angeles, CA, USA

    Bijie Bai & Aydogan Ozcan

  5. Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea

    Mahn Jae Lee

  6. Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea

    Jeongwon Shin

  7. Tomocube, Daejeon, Republic of Korea

    Dongmin Ryu, Hyun-seok Min & YongKeun Park

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Competing interests

D.H.R., M.J.L., D.R., H.-s.M. and Y.K.P. have financial interests in Tomocube, a company that commercializes holotomography and quantitative phase imaging instruments. A.O. has financial interests in Lucendi and Pictor Labs, companies that commercialize deep learning-enhanced microscopy and sensing systems for water quality and pathology applications, respectively. All other authors declare no competing interests.

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Nature Methods thanks Keisuke Goda, Jianglei Di and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Rita Strack, in collaboration with the Nature Methods team.

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Park, J., Bai, B., Ryu, D. et al. Artificial intelligence-enabled quantitative phase imaging methods for life sciences. Nat Methods 20, 1645–1660 (2023). https://doi.org/10.1038/s41592-023-02041-4

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