Cell cycle detection using phase imaging with computational specificity (PICS)

Yuchen R. He; Shenghua He; Mikhail Kandel; Young Jae Lee; Nahil Sobh; Mark Anastasio; Gabriel Popescu

doi:10.1117/12.2584432

5 March 2021 Cell cycle detection using phase imaging with computational specificity (PICS)

Yuchen R. He, Shenghua He, Mikhail Kandel, Young Jae Lee, Nahil Sobh, Mark Anastasio, Gabriel Popescu

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Proceedings Volume 11653, Quantitative Phase Imaging VII; 116531R (2021) https://doi.org/10.1117/12.2584432
Event: SPIE BiOS, 2021, Online Only

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Abstract

Quantitative phase imaging (QPI), with its capability to capture intrinsic contrast within transparent samples, has emerged as an important imaging method for biomedical research. However, due to its label-free nature, QPI lacks specificity and thus faces limitations in complex cellular systems. In our previous works, we have proposed phase imaging with computational specificity (PICS), a novel AI-enhanced imaging approach that advances QPI by utilizing deep learning for specificity. Here we present that PICS can be applied to study individual cell behavior and cellular dry mass change across different phases of the cell cycle. The cell cycle information is traditionally obtained by fluorescence microscopy with markers like Fluorescence Ubiquitin Cell Cycle Indicator (FUCCI). Our work showed that using deep learning, we can train a neural network to accurately predict the cell cycle phase (G1, S, or G2) for each individual cell.

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Yuchen R. He, Shenghua He, Mikhail Kandel, Young Jae Lee, Nahil Sobh, Mark Anastasio, and Gabriel Popescu "Cell cycle detection using phase imaging with computational specificity (PICS)", Proc. SPIE 11653, Quantitative Phase Imaging VII, 116531R (5 March 2021); https://doi.org/10.1117/12.2584432

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KEYWORDS

Luminescence

Phase imaging

Photonic integrated circuits

Microscopy

Neural networks

Image segmentation

Machine learning

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