Protein & Cell >

2019 , Vol. 10 >Issue 4: 306 - 311

DOI: 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 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
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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

References

Publishing order | Descend order by publishing year | Descend order by cited within
1
Briggman KL, Bock DD (2012) Volume electron microscopy for neuronal circuit reconstruction. Curr Opin Neurobiol 22:154–161

DOI

2
Cabrera O, Berman DM, Kenyon NS, Ricordi C, Berggrern PO, Caicedo A (2006) The unique cytoarchitecture of human pancreatic islets has implications for islet cell function. Proc Natl Acad Sci USA 103:2334–2339

DOI

3
Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2018) DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40:834–848

DOI

4
Diaz E, Ayala G, Diaz ME, Gong LW, Toomre D (2010) Automatic detection of large dense-core vesicles in secretory cells and statistical analysis of their intracellular distribution. IEEE-ACM Trans Comput Biol Bioinform 7:2–11

DOI

5
Jegou S, Drozdzal M, Vazquez D, Romero A, Bengio Y (2017) The one hundred layers tiramisu: fully convolutional DenseNets for semantic segmentation. In 2017 IEEE conference on computer vision and pattern recognition workshops (Cvprw), pp 1175–1183

DOI

6
Long J, Shelhamer E, Darrell T (2014) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640–651

7
MacDonald PE, Braun M, Galvanovskis J, Rorsman P (2006) Release of small transmitters through kiss-and-run fusion pores in rat pancreatic beta cells. Cell Metab 4:283–290

DOI

8
Masini M, Marselli L, Bugliani M, Martino L, Masiello P, Marchetti P, De Tata V (2012) Ultrastructural morphometric analysis of insulin secretory granules in human type 2 diabetes. Acta Diabetol 49 (Suppl 1):S247–S252

DOI

9
Ostenson CG, Gaisano H, Sheu L, Tibell A, Bartfai T (2006) Impaired gene and protein expression of exocytotic soluble N-ethylmaleimide attachment protein receptor complex proteins in pancreatic islets of type 2 diabetic patients. Diabetes 55:435–440

DOI

10
Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In medical image computing and computer-assisted intervention—MICCAI 2015, pp 234–241

DOI

11
Smith TC, Frank E (2016) Introducing machine learning concepts with WEKA. Methods Mol Biol 1418:353–378

DOI

12
Szegedy C, Liu W, Jia YQ, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. IEEE Conf Comput Vis Pattern Recognit 2015:1–9

DOI

13
Van Valen DA, Kudo T, Lane KM, Macklin DN, Quach NT, DeFelice MM, Maayan I, Tanouchi Y, Ashley EA, Covert MW (2016) Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments. PLoS Comput Biol 12:e1005177

DOI

14
Wijesekara N, Dai FF, Hardy AB, Giglou PR, Bhattacharjee A, Koshkin V, Chimienti F, Gaisano HY, Rutter GA, Wheeler MB (2010) Beta cell-specific Znt8 deletion in mice causes marked defects in insulin processing, crystallisation and secretion. Diabetologia 53:1656–1668

DOI

15
Xue YH, Zhao W, Du W, Zhang X, Ji G, Ying W, Xu T (2012) Ultrastructural study of insulin granules in pancreatic beta-cells of db/db mouse by scanning transmission electron microscopy tomography. Protein Cell 3:521–525

DOI

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