{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T10:31:37Z","timestamp":1785407497784,"version":"3.56.0"},"reference-count":55,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,5,24]],"date-time":"2026-05-24T00:00:00Z","timestamp":1779580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003141","name":"Consejo Nacional de Ciencia y Tecnolog\u00eda","doi-asserted-by":"publisher","award":["BP-PA-20220721145920793-2991392"],"award-info":[{"award-number":["BP-PA-20220721145920793-2991392"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]},{"award":["BP-PA-20220721145920793-2991392"],"award-info":[{"award-number":["BP-PA-20220721145920793-2991392"]}],"id":[{"id":"https:\/\/ror.org\/00aejfc65","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Viruses"],"abstract":"<jats:p>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 \u00b1 variance): Dice similarity coefficient (DSC) = 0.9345 \u00b1 0.0006; intersection over union (IoU) = 0.8782 \u00b1 0.0018; sensitivity\/true positive rate (TPR) = 0.9373 \u00b1 0.0018; specificity\/true negative rate (SPC) = 0.9517 \u00b1 0.0012; accuracy = 0.9449 \u00b1 0.0004; area under the ROC curve (AUC) = 0.9446 \u00b1 0.0431; and Cohen\u2019s Kappa = 0.9137 \u00b1 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.<\/jats:p>","DOI":"10.3390\/v18060592","type":"journal-article","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T02:32:28Z","timestamp":1779676348000},"page":"592","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cascade Semantic Segmentation by a Convolutional Neural Network in Combination with Image Super-Euclidean Pixels Processing for SARS-CoV-2 Microscopy Images"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2575-8251","authenticated-orcid":false,"given":"Santiago","family":"Tello-Mijares","sequence":"first","affiliation":[{"name":"Instituto Tecnol\u00f3gico de la Laguna, Tecnol\u00f3gico Nacional de M\u00e9xico Campus Laguna, Torre\u00f3n 27150, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francisco","family":"Flores","sequence":"additional","affiliation":[{"name":"Instituto Tecnol\u00f3gico de la Laguna, Tecnol\u00f3gico Nacional de M\u00e9xico Campus Laguna, Torre\u00f3n 27150, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fomuy","family":"Woo","sequence":"additional","affiliation":[{"name":"Medical Familiar Unit, Instituto de Seguridad y Servicios Sociales de Los Trabajadores del Estado, Torre\u00f3n 27268, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1038\/s41586-020-2008-3","article-title":"A newcoronavirusassociated with humanrespiratory disease in China","volume":"579","author":"Wu","year":"2020","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1038\/s41564-020-0695-z","article-title":"The species Severe acute respiratory syndrome-related coronavirus: Classifying 2019-nCoV and naming it SARS-CoV-2","volume":"5","author":"Gorbalenya","year":"2020","journal-title":"Nat. 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