  Cellular and molecular imaging techniques and models have been developed to
characterize single stages of viral proliferation after focal infection of
cells in vitro. The fast and automatic classification of cell imaging data may
prove helpful prior to any further comparison of representative experimental
data to mathematical models of viral propagation in host cells. Here, we use
computer generated images drawn from a reproduction of an imaging model from a
previously published study of experimentally obtained cell imaging data
representing progressive viral particle proliferation in host cell monolayers.
Inspired by experimental time-based imaging data, here in this study viral
particle increase in time is simulated by a one-by-one increase, across images,
in black or gray single pixels representing dead or partially infected cells,
and hypothetical remission by a one-by-one increase in white pixels coding for
living cells in the original image model. The image simulations are submitted
to unsupervised learning by a Self-Organizing Map (SOM) and the Quantization
Error in the SOM output (SOM-QE) is used for automatic classification of the
image simulations as a function of the represented extent of viral particle
proliferation or cell recovery. Unsupervised classification by SOM-QE of 160
model images, each with more than three million pixels, is shown to provide a
statistically reliable, pixel precise, and fast classification model that
outperforms human computer-assisted image classification by RGB image mean
computation. The automatic classification procedure proposed here provides a
powerful approach to understand finely tuned mechanisms in the infection and
proliferation of virus in cell lines in vitro or other cells.
