Increasing segmentation performance with synthetic agar plate images

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dc.contributor.authorČičatka, Michalcs
dc.contributor.authorBurget, Radimcs
dc.contributor.authorKarásek, Jancs
dc.contributor.authorLancos, Jancs
dc.coverage.issue3cs
dc.coverage.volume10cs
dc.date.issued2024-02-15cs
dc.description.abstractBackground: Agar plate analysis is vital for microbiological testing in industries like food, pharmaceuticals, and biotechnology. Manual inspection is slow, laborious, and error -prone, while existing automated systems struggle with the complexity of real -world agar plates. A shortage of diverse datasets hinders the development and evaluation of robust automated systems. Methods: In this paper, two new annotated datasets and a novel methodology for synthetic agar plate generation are presented. The datasets comprise 854 images of cultivated agar plates and 1,588 images of empty agar plates, encompassing various agar plate types and microorganisms. These datasets are an extension of the publicly available BRUKERCOLONY dataset, collectively forming one of the largest publicly available annotated datasets for research. The methodology is based on an efficient image generation pipeline that also simulates cultivation -related phenomena such as haemolysis or chromogenic reactions. Results: The augmentations significantly improved the Dice coefficient of trained U -Net models, increasing it from 0.671 to 0.721. Furthermore, training the U -Net model with a combination of real and 150% synthetic data demonstrated its efficacy, yielding a remarkable Dice coefficient of 0.729, a substantial improvement from the baseline of 0.518. UNet3+ exhibited the highest performance among the U -Net and Attention U -Net architectures, achieving a Dice coefficient of 0.767. Conclusions: Our experiments showed the methodology's applicability to real -world scenarios, even with highly variable agar plates. Our paper contributes to automating agar plate analysis by presenting a new dataset and effective methodology, potentially enhancing fully automated microbiological testingen
dc.formattextcs
dc.format.extent1-14cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationHeliyon. 2024, vol. 10, issue 3, p. 1-14.en
dc.identifier.doi10.1016/j.heliyon.2024.e25714cs
dc.identifier.issn2405-8440cs
dc.identifier.orcid0000-0002-8119-6679cs
dc.identifier.orcid0000-0003-1849-5390cs
dc.identifier.other187722cs
dc.identifier.researcheridJDC-4257-2023cs
dc.identifier.scopus58781281100cs
dc.identifier.scopus23011250200cs
dc.identifier.urihttp://hdl.handle.net/11012/245505
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofHeliyoncs
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S2405844024017456cs
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2405-8440/cs
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/cs
dc.subjectagar platesen
dc.subjectsynthetic image generationen
dc.subjectdeep learningen
dc.subjectsemantic segmentationen
dc.titleIncreasing segmentation performance with synthetic agar plate imagesen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-187722en
sync.item.dbtypeVAVen
sync.item.insts2025.03.06 11:53:35en
sync.item.modts2025.03.05 17:32:03en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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