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Detection of herpesvirus capsids in transmission electron microscopy images using transfer learning

Authors
  • Devan, K. Shaga1
  • Walther, P.1
  • von Einem, J.2
  • Ropinski, T.3
  • Kestler, H. A.4
  • Read, C.1, 2
  • 1 Ulm University, Central Facility for Electron Microscopy, Ulm, Germany , Ulm (Germany)
  • 2 Ulm University Medical Center, Institute of Virology, Ulm, Germany , Ulm (Germany)
  • 3 Ulm University, Institute of Media Informatics, Ulm, Germany , Ulm (Germany)
  • 4 Ulm University, Institute of Medical Systems Biology, Ulm, Germany , Ulm (Germany)
Type
Published Article
Journal
Histochemistry and Cell Biology
Publisher
Springer Berlin Heidelberg
Publication Date
Nov 28, 2018
Volume
151
Issue
2
Pages
101–114
Identifiers
DOI: 10.1007/s00418-018-1759-5
Source
Springer Nature
Keywords
License
Yellow

Abstract

The detailed analysis of secondary envelopment of the Human betaherpesvirus 5/human cytomegalovirus (HCMV) from transmission electron microscopy (TEM) images is an important step towards understanding the mechanisms underlying the formation of infectious virions. As a step towards a software-based quantification of different stages of HCMV virion morphogenesis in TEM, we developed a transfer learning approach based on convolutional neural networks (CNNs) that automatically detects HCMV nucleocapsids in TEM images. In contrast to existing image analysis techniques that require time-consuming manual definition of structural features, our method automatically learns discriminative features from raw images without the need for extensive pre-processing. For this a constantly growing TEM image database of HCMV infected cells was available which is unique regarding image quality and size in the terms of virological EM. From the two investigated types of transfer learning approaches, namely feature extraction and fine-tuning, the latter enabled us to successfully detect HCMV nucleocapsids in TEM images. Our detection method has outperformed some of the existing image analysis methods based on discriminative textural indicators and radial density profiles for virus detection in TEM images. In summary, we could show that the method of transfer learning can be used for an automated detection of viral capsids in TEM images with high specificity using standard computers. This method is highly adaptable and in future could be easily extended to automatically detect and classify virions of other viruses and even distinguish different virion maturation stages.

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