DOI: 10.14278/rodare.3002
title: VACVPlaque: mobile photography of Vaccinia virus plaque assay with segmentation masks

description: <strong>How to Cite Us</strong>

De, T., Thangamani, S., Urbański, A., & Yakimovich, A. (2025). A digital photography dataset for Vaccinia Virus plaque quantification using Deep Learning. <em>Scientific Data</em>, <em>12</em>(1), 719.


<pre>@article{de2025digital,
 title={A digital photography dataset for Vaccinia Virus plaque quantification using Deep Learning},
 author={De, Trina and Thangamani, Subasini and Urba{\'n}ski, Adrian and Yakimovich, Artur},
 journal={Scientific Data},
 volume={12},
 number={1},
 pages={719},
 year={2025},
 publisher={Nature Publishing Group UK London}
}</pre>


<strong>Data Description</strong>

The VACVPlaque dataset comprises spatially correlated objects, specifically virological plaques, which are circular phenotypes indicative of vaccinia virus (VACV) spread, and the wells of the assay plate. The virus plaque assay is a common method performed by infecting a monolayer of host cells (indicator cells) that are grown in the wells of assay plates or dishes. The host cells are infected with varying concentrations of a highly diluted virus inoculum. After an incubation period, typically around 48 hours, the cells are fixed with formaldehyde and stained with a dye to reveal the plaques or areas of cell death. By counting these plaques, researchers can calculate the number of infectious particles present in the original inoculum as described in [1].

This dataset consists of mobile photographs of 6-well tissue culture plates where the VACV plaque assay was conducted. The photographs were taken using two different mobile phones, resulting in 211, 8-bit RGB images with a resolution of 2448 x 3264 pixels. Each plate was photographed from two different perspectives using two different devices, meaning there are two images of the same plate but from different angles and devices.

To aid in the training of machine learning models, the dataset is divided into training, validation, and test subsets in a 70:20:10 ratio. To prevent data leaks, only one perspective of each image is included in the validation and test subsets. The training subset, which includes images from both perspectives, consists of 148 images.

<strong>File Description:</strong>

VACVPlaque_train.zip -&gt; train holdout

VACVPlaque_validation.zip -&gt; validation holdout

VACVPlaque_test.zip -&gt; test holdout

Each zip file contains:

images -&gt; {filename}.tif

plaque_masks -&gt; {filename}.tif

well_masks -&gt; {filename}.tif

 

<strong>Version 2 update</strong>

This version contains additionally models weights from the following publications:

HydraStarDist (model_vacvplaque_hsd.zip), please cite as:

<em>De, T., Thangamani, S., Urbański, A., & Yakimovich, A. (2025). A digital photography dataset for Vaccinia Virus plaque quantification using Deep Learning. Scientific Data, 12(1), 719.</em>

HydraStarDist-WBR (model_vacvplaque_hsd_wbr.zip), please cite as:

<em>De, T., Urbanski, A., & Yakimovich, A. (2025). Single-shot Star-convex Polygon-based Instance Segmentation for Spatially-correlated Biomedical Objects. arXiv preprint arXiv:2504.12078.</em>

 

 

<strong>References:</strong>

1. Dulbecco, Renato. "Production of plaques in monolayer tissue cultures by single particles of an animal virus." Proceedings of the National Academy of Sciences 38, no. 8 (1952): 747-752.