Unsupervised automatic classification of Scanning Electron Microscopy (SEM) images of CD4+ cells with varying extent of HIV virion infection

TL;DR

This paper presents an unsupervised, fully automatic classification method using Self Organized Maps to analyze SEM images of CD4+ cells with HIV, enabling reliable detection of infection progression with minimal human intervention.

Contribution

The study introduces a SOM-based automatic classification approach for SEM images of cells, demonstrating high reliability in detecting infection-related changes.

Findings

Quantization error correlates with infection extent.

Method outperforms human experts in reliability.

Fast and easy to implement.

Abstract

Archiving large sets of medical or cell images in digital libraries may require ordering randomly scattered sets of image data according to specific criteria, such as the spatial extent of a specific local color or contrast content that reveals different meaningful states of a physiological structure, tissue, or cell in a certain order, indicating progression or recession of a pathology, or the progressive response of a cell structure to treatment. Here we used a Self Organized Map (SOM)-based, fully automatic and unsupervised, classification procedure described in our earlier work and applied it to sets of minimally processed grayscale and/or color processed Scanning Electron Microscopy (SEM) images of CD4+ T-lymphocytes (so-called helper cells) with varying extent of HIV virion infection. It is shown that the quantization error in the SOM output after training permits to scale the spatial magnitude and the direction of change (+ or -) in local pixel contrast or color across images of a series with a reliability that exceeds that of any human expert. The procedure is easily implemented and fast, and represents a promising step towards low-cost automatic digital image archiving with minimal intervention of a human operator.