Cascade Semantic Segmentation by a Convolutional Neural Network in Combination with Image Super-Euclidean Pixels Processing for SARS-CoV-2 Microscopy Images

Although SARS-CoV-2 has been extensively studied from clinical, virological, and diagnostic perspectives, the problem of accurate automatic semantic segmentation of SARS-CoV-2 particles in electron microscopy images remains inadequately explored. Existing studies have largely focused on virus detection, classification, morphometry, or conventional image analysis, while comparatively little attention has been paid to pixel-level delineation of viral structures using specialised deep learning segmentation frameworks. To address this gap, we propose here a deep learning system based on convolutional neural networks (CNNs) combined with image processing techniques to establish semantic segmentation tools for the automatic identification of SARS-CoV-2. Our approach utilises the super-Euclidean pixels method as an intermediate layer within the CNN for semantic segmentation. We then compare its performance against the gradient vector flow (GVF) and Poisson inverse gradient (PIG) segmenters. The proposed CNN model surpassed the traditional GVF and PIG segmentation models, achieving the following metrics (mean ± variance): Dice similarity coefficient (DSC) = 0.9345 ± 0.0006; intersection over union (IoU) = 0.8782 ± 0.0018; sensitivity/true positive rate (TPR) = 0.9373 ± 0.0018; specificity/true negative rate (SPC) = 0.9517 ± 0.0012; accuracy = 0.9449 ± 0.0004; area under the ROC curve (AUC) = 0.9446 ± 0.0431; and Cohen’s Kappa = 0.9137 ± 0.0011. This method enables virologists to employ an automatic CNN-based segmentation tool for detecting SARS-CoV-2 and demonstrates superiority over GVF and PIG.