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

Abstract

Plaque assays are the gold standard for quantifying infectious virus, yet plaque enumeration is still routinely performed manually, making virus titration labor-intensive, subjective, and difficult to standardize across analysts and laboratories. Existing automated methods primarily address individual tasks, such as plaque segmentation or counting, but do not provide an integrated workflow from plate images to biological quantification. In this paper we present Titra, an end-to-end workflow for automated cytopathic effect (CPE)-based virus titration from standard photographs of plaque assay plates. The workflow combines automatic well detection, plaque segmentation, post-processing for instance separation, plaque counting, and plaque-forming units per millilitre (PFU/mL) estimation within a single web-based platform that also enables experiment management and expert review. The approach was evaluated using images from three viral species (Mayaro virus, Coxsackievirus B3, and vaccinia virus), two plate formats (6- and 12-well), and heterogeneous image acquisition conditions, including both a newly curated dataset and the public VACVPlaque dataset. Automated plaque counts showed strong agreement with manual annotations (Pearson correlation coefficients of 0.98 for MAYV/CVB3 and 0.88 for VACV), while PFU/mL estimates closely matched manual calculations for the MAYV/CVB3 dataset (Pearson r=0.975). Comparative experiments against U-Net, StarDist, HSD-WBR, and PyPlaque demonstrated competitive segmentation and counting performance, with the proposed approach achieving the highest Dice and mAP on the public VACVPlaque dataset.