End-to-end plaque counting and virus titration from laboratory plate images with deep learning

Eugenia Moris; Alicia Costábile; Sebastián Rey; Irene Ferreiro; Joaquín Hurtado; Lizandra Lissette Luciano; Matías Villagrán; Aisha Espino Vázquez; Jomari Ramos; Isadora Monteiro; María Victoria de Santiago; Pilar Moreno; Gonzalo Moratorio; José Ignacio Orlando

arXiv · 2605.16008

End-to-end plaque counting and virus titration from laboratory plate images with deep learning

Eugenia Moris·Alicia Costábile·Sebastián Rey·Irene Ferreiro·Joaquín Hurtado·Lizandra Lissette Luciano·Matías Villagrán·Aisha Espino Vázquez·Jomari Ramos·Isadora Monteiro·María Victoria de Santiago·Pilar Moreno·Gonzalo Moratorio·José Ignacio Orlando

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Abstract

Plaque assays remain the gold standard readout of virus infectivity; however, plaque counting from plate images is labor-intensive and prone to inter-operator variability. We present an end-to-end, computer-aided workflow for cytopathic effect-based virus titration directly from laboratory plaque assay images. The proposed approach combines two models derived from the Segment Anything Model (SAM): a SAM2-based well-segmentation module that localizes assay wells across heterogeneous imaging conditions, and a SAM-based plaque-segmentation model that detects and enumerates plaques within each well. The method was evaluated on a mixed dataset comprising private plaque assay images of Mayaro virus and Coxsackievirus B3, together with public Vaccinia virus images from the VACVPlaque dataset. The pipeline outputs per-well plaque counts, automatically computes plaque-forming units per milliliter (PFU/mL), and is integrated into a web-based platform that allows users to review results and organize experiments. On held-out plates (17 from MAYV/CVB3 and 22 from VACV), the workflow generalized across two plate formats (6-well and 12-well) and showed strong agreement with manual annotations (Pearson correlation coefficients of 0.92 for MAYV/CVB3 and 0.88 for VACV). Automated plaque counts were further compared with annotations from four independent experts, demonstrating high concordance. The proposed system will be open sourced and publicly released upon acceptance of this manuscript to enable reproducible, scalable, and audit-ready plaque assay analysis while substantially reducing manual annotation effort.

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Eugenia Moris, Alicia Costábile, Sebastián Rey, Irene Ferreiro, Joaquín Hurtado, Lizandra Lissette Luciano, Matías Villagrán, Aisha Espino Vázquez, Jomari Ramos, Isadora Monteiro, María Victoria de Santiago, Pilar Moreno, Gonzalo Moratorio, José Ignacio Orlando. 2026-05-15. End-to-end plaque counting and virus titration from laboratory plate images with deep learning. https://arxiv.org/abs/2605.16008

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