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Detail publikace
ČIČATKA, M. BURGET, R. KARÁSEK, J.
Originální název
Machine-learning Approach to Microbial Colony Localisation
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
Due to the massive expansion of the mass spectrometry, increased demands for precision and constant price growth of the human labour the optimisation of the microbial samples preparation comes into question. This paper deals with designing and implementing an image processing pipeline that takes an input in the form of a Petri dish image with cultivated colonies and outputs the position of possible sampling points. In total 547 samples were collected. The first block of the pipeline consists of a trained customised ENet model which predicts a binary mask. Architectures U-Net, UNet++ and ENet were examined, where ENet was found to perform with the highest Dice coefficient (0.979).
Klíčová slova
ENet; mass spectrometry; microbial colonies; U-Net; UNet++
Autoři
ČIČATKA, M.; BURGET, R.; KARÁSEK, J.
Vydáno
15. 7. 2022
Nakladatel
IEEE
ISBN
978-1-6654-2933-7
Kniha
45th International Conference on Telecommunications and Signal Processing (TSP 2022). IEEE, 2022
Strany od
206
Strany do
211
Strany počet
6
URL
https://ieeexplore.ieee.org/document/9851236
BibTex
@inproceedings{BUT178628, author="Michal {Čičatka} and Radim {Burget} and Jan {Karásek}", title="Machine-learning Approach to Microbial Colony Localisation", booktitle="45th International Conference on Telecommunications and Signal Processing (TSP 2022). IEEE, 2022", year="2022", pages="206--211", publisher="IEEE", doi="10.1109/TSP55681.2022", isbn="978-1-6654-2933-7", url="https://ieeexplore.ieee.org/document/9851236" }