Pixel precise unsupervised detection of viral particle proliferation in cellular imaging data

Area of Science:

• * Cellular and molecular imaging
• * Virology
• * Computational biology

Background:

• * Characterizing viral proliferation in vitro requires detailed cell imaging analysis.
• * Existing methods for classifying cell imaging data can be slow and subjective.
• * Mathematical models of viral propagation benefit from accurate, quantitative imaging data.

Purpose of the Study:

• * To develop a fast and automatic classification method for cell imaging data representing viral proliferation.
• * To compare the performance of unsupervised learning (Self-Organizing Map) against human-assisted classification.
• * To provide a reliable tool for analyzing viral infection and cell recovery dynamics.

Main Methods:

• * Simulated cell imaging data based on a published viral proliferation model.
• * Employed Self-Organizing Map (SOM) for unsupervised machine learning classification.
• * Utilized Quantization Error in SOM output (SOM-QE) for image classification based on viral load and cell recovery.

Main Results:

• * SOM-QE provided statistically reliable, pixel-precise classification of 160 simulated images.
• * The automated SOM-QE method demonstrated superior speed and accuracy compared to human classification.
• * The classification effectively distinguished between viral proliferation and hypothetical cell recovery.

Conclusions:

• * Unsupervised classification using SOM-QE offers a powerful and efficient approach for analyzing viral infection dynamics in cell cultures.
• * This method enhances the understanding of virus-host cell interactions.
• * The technique is applicable to in vitro cell line studies and potentially other cell imaging analyses.