Artificial intelligence-enabled quantitative phase imaging methods for life sciences.

Journal: Nature methods
Published Date: Oct 23, 2023

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.

Authors

  • Juyeon Park
    2Department of Food Science and Technology, Yeungnam University, Gyeongsan, Gyeongsanbuk-do 38541 Republic of Korea.
  • Bijie Bai
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • DongHun Ryu
  • Tairan Liu
    Department of Mechanical Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.
  • Chungha Lee
    Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
  • Yi Luo
    Electrical and Computer Engineering Department, Bioengineering Department, University of California, Los Angeles, CA 90095 USA, and also with the California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA.
  • Mahn Jae Lee
    KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea.
  • Luzhe Huang
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.
  • Jeongwon Shin
    Infinigru, Seoul, Republic of Korea.
  • Yijie Zhang
    Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China; School of Psychology, South China Normal University, Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China. Electronic address: fanfandez@163.com.
  • Dongmin Ryu
    Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Yuzhu Li
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Geon Kim
    Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
  • Hyun-Seok Min
    1 Department of Cardiology University of Ulsan College of Medicine Asan Medical Center Seoul Korea.
  • Aydogan Ozcan
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.
  • YongKeun Park
    Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea. yk.park@kaist.ac.kr.

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