Advertisement

\(\beta \)-Hemolysis Detection on Cultured Blood Agar Plates by Convolutional Neural Networks

  • Mattia Savardi
  • Sergio Benini
  • Alberto Signoroni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

The recent introduction of Full Laboratory Automation (FLA) systems in Clinical Microbiology opens to the availability of huge streams of high definition diagnostic images representing bacteria colonies on culturing plates. In this context, the presence of \(\beta \)-hemolysis is a key diagnostic sign to assess the presence and virulence of pathogens like streptococci and to characterize major respiratory tract infections. Since it can manifest in a high variety of shapes, dimensions and intensities, obtaining a reliable automated detection of \(\beta \)-hemolysis is a challenging task, never been tackled so far in its real complexity. To this aim, here we follow a deep learning approach operating on a database of 1500 fully annotated dual-light (top-lit and back-lit) blood agar plate images collected from FLA systems operating in ordinary clinical conditions. Patch-based training and test sets are obtained with the help of an ad-hoc, total recall, region proposal technique. A DenseNet Convolutional Neural Network architecture, dimensioned and trained to classify patch candidates, achieves a 98.9% precision with a recall of 98.9%, leading to an overall 90% precision and 99% recall on a plate basis, where false negative occurrence needs to be minimized. Being the first approach able to detect \(\beta \)-hemolysis on a whole plate basis, the obtained results open new opportunities for supporting diagnostic decisions, with an expected high impact on the efficiency and accuracy of the laboratory workflow.

Keywords

Digital Microbiology Imaging Full Laboratory Automation Beta-hemolysis detection Convolutional Neural Networks Image classification 

References

  1. 1.
    Bourbeau, P.P., Ledeboer, N.A.: Automation in clinical microbiology. J. Clin. Microbiol. 51(6), 1658–1665 (2013)CrossRefGoogle Scholar
  2. 2.
    Doern, C., Holfelder, M.: Automation and design of the clinical microbiology laboratory. Manual of Clinical Microbiology (2015)Google Scholar
  3. 3.
    Hogg, S.: Essential Microbiology. Wiley, Chichester (2005)Google Scholar
  4. 4.
    Jorgensen, J., Pfaller, M., Carroll, K.: Manual of Clinical Microbiology, 11th edn. ASM Press, Washington (2015)Google Scholar
  5. 5.
    Savardi, M., Ferrari, A., Signoroni, A.: Automatic hemolysis identification on aligned dual-lighting images of cultured blood agar plates. Comput. Methods Programs Biomed. 156, 13–24 (2017)CrossRefGoogle Scholar
  6. 6.
    Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)CrossRefGoogle Scholar
  7. 7.
    Ferrari, A., Lombardi, S., Signoroni, A.: Bacterial colony counting by convolutional neural networks. In: Proceedings of the IEEE Engineering in Medicine and Biology Society Conference, pp. 7458–7461 (2015)Google Scholar
  8. 8.
    Turra, G., Arrigoni, S., Signoroni, A.: CNN-based identification of hyperspectral bacterial signatures for digital microbiology. In: Battiato, S., Gallo, G., Schettini, R., Stanco, F. (eds.) ICIAP 2017. LNCS, vol. 10485, pp. 500–510. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-68548-9_46CrossRefGoogle Scholar
  9. 9.
    Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115 (2017)CrossRefGoogle Scholar
  10. 10.
    Xie, Y., Xing, F., Shi, X., Kong, X., Su, H., Yang, L.: Efficient and robust cell detection: A structured regression approach. Med. Image Anal. 44, 245–254 (2018)CrossRefGoogle Scholar
  11. 11.
    Li, C., Wang, X., Liu, W., Latecki, L.J.: Deepmitosis: mitosis detection via deep detection, verification and segmentation networks. Med. Image Anal. 45, 121–133 (2018)CrossRefGoogle Scholar
  12. 12.
    Kooi, T., et al.: Large scale deep learning for computer aided detection of mammographic lesions. Med. Image Anal. 35, 303–312 (2017)CrossRefGoogle Scholar
  13. 13.
    Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, p. 3 (2017)Google Scholar
  14. 14.
    Wang, D., Khosla, A., Gargeya, R., Irshad, H., Beck, A.H.: Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718 (2016)

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Mattia Savardi
    • 1
  • Sergio Benini
    • 1
  • Alberto Signoroni
    • 1
  1. 1.Department of Information EngineeringUniversity of BresciaBresciaItaly

Personalised recommendations