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ČIČATKA, M. BURGET, R. KARÁSEK, J. LANCOS, J.
Originální název
Increasing segmentation performance with synthetic agar plate images
Typ
článek v časopise ve Web of Science, Jimp
Jazyk
angličtina
Originální abstrakt
Background: 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 testing
Klíčová slova
agar plates, synthetic image generation, deep learning, semantic segmentation
Autoři
ČIČATKA, M.; BURGET, R.; KARÁSEK, J.; LANCOS, J.
Vydáno
15. 2. 2024
Nakladatel
Elsevier
ISSN
2405-8440
Periodikum
Heliyon
Ročník
10
Číslo
3
Stát
Spojené státy americké
Strany od
1
Strany do
14
Strany počet
URL
https://www.sciencedirect.com/science/article/pii/S2405844024017456
Plný text v Digitální knihovně
http://hdl.handle.net/11012/245505
BibTex
@article{BUT187722, author="Michal {Čičatka} and Radim {Burget} and Jan {Karásek} and Jan {Lancos}", title="Increasing segmentation performance with synthetic agar plate images", journal="Heliyon", year="2024", volume="10", number="3", pages="1--14", doi="10.1016/j.heliyon.2024.e25714", issn="2405-8440", url="https://www.sciencedirect.com/science/article/pii/S2405844024017456" }