Virus Particle Detection by Convolutional Neural Network in Transmission Electron Microscopy Images

Eisuke Ito, Takaaki Sato, Daisuke Sano, Etsuko Utagawa, Tsuyoshi Kato

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


A new computational method for the detection of virus particles in transmission electron microscopy (TEM) images is presented. Our approach is to use a convolutional neural network that transforms a TEM image to a probabilistic map that indicates where virus particles exist in the image. Our proposed approach automatically and simultaneously learns both discriminative features and classifier for virus particle detection by machine learning, in contrast to existing methods that are based on handcrafted features that yield many false positives and require several postprocessing steps. The detection performance of the proposed method was assessed against a dataset of TEM images containing feline calicivirus particles and compared with several existing detection methods, and the state-of-the-art performance of the developed method for detecting virus was demonstrated. Since our method is based on supervised learning that requires both the input images and their corresponding annotations, it is basically used for detection of already-known viruses. However, the method is highly flexible, and the convolutional networks can adapt themselves to any virus particles by learning automatically from an annotated dataset.

Original languageEnglish
Pages (from-to)201-208
Number of pages8
JournalFood and Environmental Virology
Issue number2
Publication statusPublished - 2018 Jun 1


  • Convolutional neural network
  • Feline calicivirus
  • Image processing
  • Machine learning
  • Transmission electron microscopy
  • Virus detection


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