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Artificial intelligence-enabled quantitative phase imaging methods for life sciences
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AI
DOI：10.1038/s41592-023-02041-4.png)
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Abstract
En 中文
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. This Perspective introduces advances in quantitative phase imaging and artificial intelligence-based image analysis and further describes how the two technologies intersect and synergize to enable biomedical research.
Keywords:
DEEP LEARNING APPROACH
OPTICAL DIFFRACTION TOMOGRAPHY
DIGITAL HOLOGRAPHIC MICROSCOPY
REFRACTIVE-INDEX
FLOW-CYTOMETRY
WIDE-FIELD
DYNAMICS
RECONSTRUCTION
ILLUMINATION
SEGMENTATION
