Clustering Microbial Material on Agar Plates: A Modular System Approach

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Clustering Microbial Material on Agar Plates: A Modular System Approach

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Clustering Microbial Material on Agar Plates: A Modular System Approach

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Array

ISSN

2590-0056

e-ISSN

—

Svazek periodika

—

Číslo periodika v rámci svazku

28

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

36

Strana od-do

1-36

Kód UT WoS článku

001590506600001

EID výsledku v databázi Scopus

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