Ms. Jie Zhou Profile

Jie Zhou

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Proceedings Article | 5 December 2024 Paper

Multiplex imaging flow cytometry with content-aware image restoration deep learning enhances cell imaging quality

Zhiwen Wang, Jie Zhou, Qiao Liu, Limei Wang, Xuantao Su

Proceedings Volume 13418, 134183R (2024) https://doi.org/10.1117/12.3048765

KEYWORDS: Flow cytometry, Image enhancement, Deep learning, Biomedical optics, Analytical research

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Imaging flow cytometry (IFC) has become an established tool for cell analysis across diverse biomedical fields. However, the performance of IFC is severely limited by the fundamental trade-off among multi-color, flow speed and exposure time. Here we develop multiplex imaging flow cytometry (mIFC) that overcomes this trade-off by utilizing unique single-source single-detector technology for sensitive detection of ovarian cancer cells with the content-aware image restoration method. Our mIFC achieves efficient, non-interfering 4-channel excitation and 3-channel emission based on a metal halide lamp. The spatial wavelength division multiplexing technology with a knife-edge right-angle prism is the key optical design to simultaneously obtain brightfield and multi-color fluorescence images of individual cells in flow on a single detector. A U-net variant deep learning network based on a 3-layer encoder-decoder structure is employed to perform content-aware image restoration on captured multiplex ovarian cell images. The blurred multiplex images are converted into enhanced images, which helps to balance the trade-off between flow speed and exposure time. Our multiplex imaging flow cytometry (mIFC) with content-aware image restoration deep learning method enables automatic, high-quality detection of ovarian cancer cells and has the potential broad applications in biomedical fields.

Proceedings Article | 12 March 2024 Presentation + Paper

Development of a single-detector imaging flow cytometer for light scattering and fluorescence dual-modality analysis with deep learning

Zhiwen Wang, Jie Zhou, Qiao Liu, Xuantao Su

Proceedings Volume 12846, 1284608 (2024) https://doi.org/10.1117/12.3001191

KEYWORDS: Biomedical optics, Deep learning, Light scattering, Microspheres, Fluorescence imaging, Flow cytometry, Fluorescence, Ovarian cancer, Mie scattering, Imaging systems

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Imaging flow cytometry (IFC) has been widely applied in biomedical research due to its numerous advantages, including multiparametric analysis, microscopic imaging and high-throughput detection. Previous research in our lab has demonstrated the effectiveness of two-dimensional light scattering (LS) and brightfield (BF) dual-modality imaging techniques for detecting and distinguishing unlabeled cells. As fluorescence (FL) imaging techniques are sensitive to specifically labeled cells, here we introduce a single-detector IFC enabling simultaneous imaging of LS signals and BF/FL signals for automatic single-cell analysis with deep learning. The special optical design with a knife-edge right angle (KERA) prism is adopted to simultaneously capture corresponding LS patterns in defocus and BF/FL patterns in focus on a single detector. The LS and BF dual-modality flow imaging results of 2 μm and 3.87 μm unlabeled microspheres can be obtained by our system, which can also simultaneously acquire LS and FL results for fluorescent microspheres of 2 μm and 4 μm in diameter. The results of these beads demonstrate excellent agreement between LS patterns and Mie scattering simulations. The obtained LS and BF dual-modality cell images of A2780 and Hey cells are analyzed using a visual geometry group 19 (VGG19) deep learning method through feature extraction and fusion to show accurate classification of ovarian cancer cell subtypes. In conclusion, our development of a single-detector imaging flow cytometer enables the simultaneous capture of two-dimensional light-scattering and fluorescence/brightfield images, where an automatic analysis with deep learning can be performed, showcasing potential wide applications in biomedicine.

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