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A unified deep-learning network to accurately segment insulin granules of different animal models imaged under different electron microscopy methodologies

  • Xiaoya Zhang 1 ,
  • Xiaohong Peng 1,4 ,
  • Chengsheng Han 1 ,
  • Wenzhen Zhu 1 ,
  • Lisi Wei 1 ,
  • Yulin Zhang 1 ,
  • Yi Wang 1 ,
  • Xiuqin Zhang 1 ,
  • Hao Tang 3 ,
  • Jianshe Zhang 5 ,
  • Xiaojun Xu 2 ,
  • Fengping Feng 2,5 ,
  • Yanhong Xue , 2 ,
  • Erlin Yao , 3 ,
  • Guangming Tan 3 ,
  • Tao Xu 2,3 ,
  • Liangyi Chen 1
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  • 1. State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, Peking University, Beijing 100871, China
  • 2. National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
  • 3. University of Chinese Academy of Sciences, Beijing 100049, China
  • 4. Drug Discovery Center, Key Laboratory of Chemical Genomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
  • 5. Marine Science College of Zhejiang Ocean University, National Engineering Research Center of Marine Facilities Aquaculture, Zhoushan 316022, China

Published date: 12 Apr 2019

Copyright

2018 The Author(s) 2018

Cite this article

Xiaoya Zhang , Xiaohong Peng , Chengsheng Han , Wenzhen Zhu , Lisi Wei , Yulin Zhang , Yi Wang , Xiuqin Zhang , Hao Tang , Jianshe Zhang , Xiaojun Xu , Fengping Feng , Yanhong Xue , Erlin Yao , Guangming Tan , Tao Xu , Liangyi Chen . A unified deep-learning network to accurately segment insulin granules of different animal models imaged under different electron microscopy methodologies[J]. Protein & Cell, 2019 , 10(4) : 306 -311 . DOI: 10.1007/s13238-018-0575-y

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