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

References

[1] Briggman KL, Bock DD (2012) Volume electron microscopy for neuronal circuit reconstruction. Curr Opin Neurobiol 22:154–161

[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

[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

[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

[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

[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

[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

[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

[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

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

[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

[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

[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

[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

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2018 The Author(s) 2018