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

doi:10.1007/s13238-018-0575-y

Protein Cell

2019, Vol. 10

Issue (4) : 306-311 https://doi.org/10.1007/s13238-018-0575-y

LETTER

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

Xiaoya Zhang¹, Xiaohong Peng^1,⁴, Chengsheng Han¹, Wenzhen Zhu¹, Lisi Wei¹, Yulin Zhang¹, Yi Wang¹, Xiuqin Zhang¹, Hao Tang³, Jianshe Zhang⁵, Xiaojun Xu², Fengping Feng^2,⁵, Yanhong Xue²(

), Erlin Yao³(

), Guangming Tan³, Tao Xu^2,³, Liangyi Chen¹

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

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Corresponding Author(s): Yanhong Xue,Erlin Yao

Issue Date: 12 April 2019

Cite this article:

Xiaoya Zhang,Xiaohong Peng,Chengsheng Han, et al. 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.

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https://academic.hep.com.cn/pac/EN/10.1007/s13238-018-0575-y
https://academic.hep.com.cn/pac/EN/Y2019/V10/I4/306

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