Clustering Microbial Material on Agar Plates: A Modular System Approach

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0198603" target="_blank" >RIV/00216305:26220/26:0198603 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.array.2025.100503" target="_blank" >https://doi.org/10.1016/j.array.2025.100503</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.array.2025.100503" target="_blank" >10.1016/j.array.2025.100503</a>

Result language

angličtina

Original language name

Clustering Microbial Material on Agar Plates: A Modular System Approach

Original language description

Analyzing microbial colonies on agar plates is a critical task in microbiology, with applications spanning research, diagnostics, and industry. Despite advances in automated systems, challenges remain in accurately segmenting and clustering colonies due to variability in their appearance and distribution. To address these challenges, we present a fully automated modular system for colony segmentation and clustering, combining state-of-the-art deep learning for segmentation and machine learning for cluster count prediction and clustering. The main contribution of this work is the proposition, development and evaluation of a novel system, which achieved a V-measure of 0.532 under real-world conditions, improving to 0.727 with ideal segmentation and cluster counts, setting a new benchmark for microbiological analysis. At the core of the system we propose AgarNet, an Attention U-Net-based architecture combined with an EfficientNetB4 backbone, which achieved an F1-score of 0.906 for segmentation. We also introduce a new BRUKERCLUSTER dataset, which is one of the largest and most diverse annotated resources of its kind, featuring expert-annotated images from a controlled cultivation. By combining the dataset, robust segmentation, accurate cluster count prediction, and effective clustering, the proposed system delivers a scalable solution for advancing automated microbial colony analysis. These results establish a new benchmark for automated colony clustering in microbiology, and by releasing the unique BRUKERCLUSTER dataset, we provide a valuable tool for future advancements in automated colony analysis.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10200 - Computer and information sciences

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Array

ISSN

2590-0056

e-ISSN

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Volume of the periodical

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Issue of the periodical within the volume

28

Country of publishing house

US - UNITED STATES

Number of pages

36

Pages from-to

1-36

UT code for WoS article

001590506600001

EID of the result in the Scopus database

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