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Journal of Innovative Optical Health Sciences, Volume. 15, Issue 5, 2250031(2022)

U-Net-based deep learning for tracking and quantitative analysis of intracellular vesicles in time-lapse microscopy images

Zhichao Liu1,2, Heng Zhang1, Luhong Jin1,2, Jincheng Chen1,2, Alexander Nedzved3, Sergey Ablameyko3, Qing Ma4, Jiahui Yu5, and Yingke Xu1,2,5,6、*
Author Affiliations
  • 1Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou 310027, P. R. China
  • 2Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 310027, P. R. China
  • 3National Academy of Sciences, United Institute of Informatics Problems, Belarusian State University, Minsk 220012, Republic of Belarus
  • 4Hangzhou Dowell Photonics Measurement Company Limited, Hangzhou 310000, P. R. China
  • 5Binjiang Institute of Zhejiang University, Hangzhou 310053, P. R. China
  • 6Department of Endocrinology, Children’s Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Children’s Health, Hangzhou, 310051 China
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    Zhichao Liu, Heng Zhang, Luhong Jin, Jincheng Chen, Alexander Nedzved, Sergey Ablameyko, Qing Ma, Jiahui Yu, Yingke Xu. U-Net-based deep learning for tracking and quantitative analysis of intracellular vesicles in time-lapse microscopy images[J]. Journal of Innovative Optical Health Sciences, 2022, 15(5): 2250031

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    Paper Information

    Category: Research Articles

    Received: Jan. 22, 2022

    Accepted: Jun. 5, 2022

    Published Online: Oct. 24, 2022

    The Author Email: Yingke Xu (yingkexu@zju.edu.cn)

    DOI:10.1142/S1793545822500316

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology