Artificial intelligence-enabled quantitative phase imaging methods for life sciences

Juyeon Park, Bijie Bai, DongHun Ryu, Tairan Liu, Chungha Lee, Yi Luo, Mahn Jae Lee, Luzhe Huang, Jeongwon Shin, Yijie Zhang, Dongmin Ryu, Yuzhu Li, Geon Kim, Hyun-seok Min, Aydogan Ozcan, and YongKeun Park
Nature Methods 20 2023 1645–1660


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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