Virus Particle Detection by Convolutional Neural Network in Transmission Electron Microscopy Images
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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.
KeywordsFeline calicivirus Virus detection Transmission electron microscopy Image processing Convolutional neural network Machine learning
This work was supported by the Japan Society for the Promotion of Science through Grants-in-Aid for Scientific Research (A) (17H01299) and (C) (15K00591).
- Bradski, G. (2000). The OpenCV library. Dr. Dobb’s Journal of Software Tools, 120, 122–125.Google Scholar
- Ciresan, D., Giusti, A., Gambardella, L. M., & Schmidhuber, J. (2012). Deep neural networks segment neuronal membranes in electron microscopy images. In Proceedings of the Advances in Neural Information Processing Systems (pp. 2843–2851).Google Scholar
- Ciresan, D. C., Giusti, A., Gambardella, L. M., & Schmidhuber, J. (2013). Mitosis detection in breast cancer histology images with deep neural networks. In International Conference on Medical Image Computing and Computer-assisted Intervention (pp. 411–418). Berlin, Heidelberg: Springer.Google Scholar
- Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv Preprint arXiv:1207.0580.
- Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv Preprint arXiv:1502.03167.
- Ishii, S., Kitamura, G., Segawa, T., Kobayashi, A., Miura, T., Sano, D., et al. (2014). Microfluidic quantitative PCR for simultaneous quantification of multiple viruses in environmental water samples. Applied and Environmental Microbiology, 80(24), 7505–7511.CrossRefPubMedPubMedCentralGoogle Scholar
- Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Proceedings of the Advances in Neural Information Processing Systems (pp. 1097–1105).Google Scholar
- Kylberg, G., Uppström, M., & Sintorn, I. M. (2011). Virus texture analysis using local binary patterns and radial density profiles. In Proceedings of the Iberoamerican Congress on Pattern Recognition (pp. 573–580). Berlin, Heidelberg: Springer.Google Scholar
- Matuszewski, B. J. & Shark, L. K. (2001). Hierarchical iterative Bayesian approach to automatic recognition of biological viruses in electron microscope images. In Proceedings of the 2001 International Conference on Image Processing (Vol. 2, pp. 347–350). IEEE.Google Scholar
- Matuszewski, B., Shark, L. K., & Hall, G. (1997). A novel segmentation and classification method for identification of viruses in electron microscope images. In Proceedings of the Sixth International Conference on Image Processing and Its Applications (Vol. 2, pp. 819–823). IET.Google Scholar
- Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.Google Scholar
- Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1), 1929–1958.Google Scholar
- Thi Nguyen, H.-T., Nakagomi, T., Sano, D., Sherchand, J. B., Pandey, B. D., Cunliffe, N. A., et al. (2015). Molecular epidemiology of noroviruses detected in Nepalese children with acute diarrhea between 2005 and 2011: Increase and predominance of minor genotype GII.13. Infection, Genetics and Evolution, 30, 27–36.CrossRefGoogle Scholar
- Wan, L., Zeiler, M., Zhang, S., Cun, Y. L., & Fergus, R. (2013). Regularization of neural networks using DropConnect. In Proceedings of the 30th International Conference on Machine Learning (ICML-13) (pp. 1058–1066).Google Scholar