Lightweight automatized species and media-invariant colony counting application

Open Research Europe 2026 Open access

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Authors: Thibaut Soubrié, Vadym Zhytniuk, Tetiana Holdanova, Oleh Mezhenskyi, Alexander J. Cunliffe, James Redfern
DOI: 10.12688/openreseurope.23934.1
Cited by: 1
References: 5

Abstract

Colony counting is a routine procedure in the microbiology field, which is labor-intensive, time-consuming, and prone to human-introduced variations, which can hinder assay accuracy. A simple way to overcome this issue is to use an automated computer system capable of performing fast, accurate, and repeatable colony counting. Historically, for this task, the two most common techniques used were simple edge detection, and generic convolutional neural networks, capable of image processing. In this article, we present a simple desktop application based on a lightweight, accurate deep learning model fine-tuned for colony counting under different conditions.

Fields of Study & Subjects

Cell Image Analysis Techniques Digital Imaging for Blood Diseases AI in cancer detection

Publication details

Journal / source
Open Research Europe
Publisher
CERN
Volume
6
Pages
372–372
Publication date
23 September 2026
Language
EN