Advertisement

Fluorescence microscopy image classification of 2D HeLa cells based on the CapsNet neural network

  • XiaoQing ZhangEmail author
  • Shu-Guang Zhao
Original Article

Abstract

The development of computer technology now allows the quick and efficient automatic fluorescence microscopy generation of a large number of images of proteins in specific subcellular compartments using fluorescence microscopy. Digital image processing and pattern recognition technology can easily classify these images, identify the subcellular location of proteins, and subsequently carry out related work such as analysis and investigation of protein function. Here, based on a fluorescence microscopy 2D image dataset of HeLa cells, the CapsNet network model was used to classify ten types of images of proteins in different subcellular compartments. Capsules in the CapsNet network model were trained to capture the possibility of certain features and variants rather than to capture the characteristics of a specific variant. The capsule at the same level predicted the instantiation parameters of the higher level capsule through the transformation matrix, and the higher level capsule became active when multiple dynamic routing forecasts were consistent. Experiments show that using the CapsNet network model to classify 2D HeLa datasets can achieve higher accuracy.

Graphical abstract

Keywords

CapsNet Subcellular localization Fluorescence microscopy 2D HeLa Image classification Neural network 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Boland MV, Murphy RF (2001) A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells. Bioinformatics 17(12):1213–1223CrossRefGoogle Scholar
  2. 2.
    Huang K, Murphy RF (2004) Boosting accuracy of automated classification of fluorescence microscope images for location proteomics. BMC Bioinformatics 5:78CrossRefGoogle Scholar
  3. 3.
    Chen X (2006) Automated interpretation of subcellular patterns in fluorescence microscope images for location proteomics. Cytometry 69(7):631–640CrossRefGoogle Scholar
  4. 4.
    Nanni L, Lumini A, Brahnam S (2010) Local binary patterns variants as texture descriptors for medical image analysis. Artif Intell Med 49(2):117–125CrossRefGoogle Scholar
  5. 5.
    Godinez WJ, Hossain I, Lazic SE (2017) A multi-scale convolutional neural network for Phenotyping high-content cellular images. Bioimage Imform 33(13):2010–2019Google Scholar
  6. 6.
    Pärnamaa T, Parts L (2017) Accurate classification of protein subcellular localization from high throughput microscopy images using deep learning. G3 7(5):1385–1392CrossRefGoogle Scholar
  7. 7.
    Tahir M (2018) Pattern analysis of protein images from fluorescence microscopy using gray level co-occurrence matrix. J King Saud Univ Sci 30(1):29–40CrossRefGoogle Scholar
  8. 8.
    Sabour, S., Nov, C. V, and Hinton, G. E. (2017). Dynamic routing between capsules. Computer vision and pattern recognition, (Nips)Google Scholar
  9. 9.
    Boland MV, Markey MK, Murphy RF (1998) Automated recognition of patterns characteristic of subcellular structures in fluorescence microscopy images. Cytometry 33(3):366–375CrossRefGoogle Scholar
  10. 10.
    Murphy RF, Velliste M, Porreca G (2003) Robust numerical features for description and classification of subcellular location patterns in fluorescence microscope images. J VLSI Sig Proc Syst 35(3):311–321CrossRefGoogle Scholar
  11. 11.
    Mellman I (1996) Endocytosis and molecular sorting. Annu Rev Cell Dev Biol 12:575–625.  https://doi.org/10.1146/annurev.cellbio.12.1.575 CrossRefGoogle Scholar
  12. 12.
    Pavelk M, Mironov AA (2008) The Golgi apparatus: state of the art 110 years after Camillo Golgi’s discovery. Springer, Berlin, p 580 ISBN978-3-211-76310-0Google Scholar
  13. 13.
    Settembre C, Fraldi A, Medina DL, Ballabio A (2013) Signals from the lysosome: a control centre for cellular clearance and energy metabolism. Nat Rev Mol Cell Biol 14(5):283–296.  https://doi.org/10.1038/nrm3565 PMC 4387238CrossRefGoogle Scholar
  14. 14.
    "Archived copy".Archivedfrom the original on 2014-02-06. Retrieved2014-02-24Google Scholar
  15. 15.
    Vale RD (2003) The molecular motor toolbox for intracellular transport. Cell 112(4):467–480.  https://doi.org/10.1016/S0092-8674(03)00111-9.PMID12600311 CrossRefGoogle Scholar
  16. 16.
    Campbell NA, Williamson B, Heyden RJ (2006) Biology: exploring life. Pearson prentice Hall, Boston, Massachusetts ISBN978-0-13-250882-7Google Scholar
  17. 17.
    McBride HM, Neuspiel M, Wasiak S (July 2006) Mitochondria: more than just a powerhouse. Curr Biol 16(14):R551–R560.  https://doi.org/10.1016/j.cub.2006.06.054.PMID16860735 CrossRefGoogle Scholar
  18. 18.
    Valero T (2014) Mitochondrial biogenesis: pharmacological approaches. Curr Pharm Des 20(35):5507–5509.  https://doi.org/10.2174/138161282035140911142118 CrossRefGoogle Scholar
  19. 19.
    Sanchis-Gomar F, García-Giménez JL, Gómez-Cabrera MC, Pallardó FV (2014) Mitochondrial biogenesis in health and disease. Molecular and therapeutic approaches. Curr Pharm Des 20(35):5619–5633.  https://doi.org/10.2174/1381612820666140306095106 CrossRefGoogle Scholar
  20. 20.
    O’Sullivan JM, Pai DA, Cridge AG, Engelke DR, Ganley AR (2013) The nucleolus: a raft adrift in the nuclear sea or the keystone in nuclear structure? Biomol Concepts 4(3):277–286.  https://doi.org/10.1515/bmc-2012-0043.PMC5100006.PMID25436580 Google Scholar
  21. 21.
    Olson MO, Dundr M (2015) Nucleolus: structure and function. Encyclopedia Life Sci (eLS).  https://doi.org/10.1002/9780470015902.a0005975.pub3.ISBN978-0-470-01617-6
  22. 22.
    Li, C., and Huang, J. (2013). A novel method for cell phenotype image classification. 3rd international conference on electric and electronics (EEIC 2013), (Eeic), 105–107Google Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  1. 1.College of Information Science and TechnologyDonghua UniversityShanghaiChina
  2. 2.Nanjing University of Chinese Medicine Hanlin CollegeTaizhouChina

Personalised recommendations