From Segmentation to Clustering: Advancing Algorithms for Grouping Microbial Colonies on Agar Plates

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU149460" target="_blank" >RIV/00216305:26220/23:PU149460 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

From Segmentation to Clustering: Advancing Algorithms for Grouping Microbial Colonies on Agar Plates

Popis výsledku v původním jazyce

Agar plates are essential in microbiology, yet manual intervention remains common for tasks like colony-picking, especially in smaller laboratories. This paper addresses the research gap of clustering microbes within agar plate images to advance automated solutions. Three innovative clustering approaches based on visual properties are presented. Using synthetic agar plate images with annotated microbes, these algorithms are developed and evaluated, with performance assessed using the V-measure metric. Results reveal challenges in pixel-level clustering without segmentation masks (V-measure: 0.284), while U-Net autoencoder features with OPTICS show reasonable performance (V-measure: 0.705). K-means with a colony extraction pipeline excels (V-measure: 0.942), emphasizing the importance of high-level colony features. These solutions offer potential enhancements for colony-picking robots and advanced analysis. This research significantly contributes to agar plate analysis, paving the way for future automated colony analysis and streamlined lab workflows.

Název v anglickém jazyce

From Segmentation to Clustering: Advancing Algorithms for Grouping Microbial Colonies on Agar Plates

Popis výsledku anglicky

Agar plates are essential in microbiology, yet manual intervention remains common for tasks like colony-picking, especially in smaller laboratories. This paper addresses the research gap of clustering microbes within agar plate images to advance automated solutions. Three innovative clustering approaches based on visual properties are presented. Using synthetic agar plate images with annotated microbes, these algorithms are developed and evaluated, with performance assessed using the V-measure metric. Results reveal challenges in pixel-level clustering without segmentation masks (V-measure: 0.284), while U-Net autoencoder features with OPTICS show reasonable performance (V-measure: 0.705). K-means with a colony extraction pipeline excels (V-measure: 0.942), emphasizing the importance of high-level colony features. These solutions offer potential enhancements for colony-picking robots and advanced analysis. This research significantly contributes to agar plate analysis, paving the way for future automated colony analysis and streamlined lab workflows.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/VK01010107" target="_blank" >VK01010107: Aplikace umělé inteligence pro forenzní identifikaci stop zeminových fází</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

ISBN

979-8-3503-9328-6

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

176-181

Název nakladatele

Neuveden

Místo vydání

Ghent

Místo konání akce

Gent, Belgium

Datum konání akce

30. 10. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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