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VÝVODA, J. JAKUBÍČEK, R.
Originální název
Comparison of full-size and patches-based learning approaches for aneurysm segmentation in TOF-MRI data
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
The paper is interested in segmentation of intracranial aneurysms. Intracranial aneurysms are life-threatening issue. In this paper there are proposed two methods for this segmentation problem. First one is segmentation with use of full size images, the other one uses patches of the image, which could help decrease the ration between pixels representing background and pixels representing aneurysms. Data from ADAM challenge 2020 are used to train and evaluate these approaches. Using full images show better results in dice coefficient, which is 0.16 greater, then patched image approach.
Klíčová slova
Intracranial aneurysm, aneurysm, machine learning, detection, magnetic resonance, U-net, segmentation
Autoři
VÝVODA, J.; JAKUBÍČEK, R.
Vydáno
26. 4. 2022
Nakladatel
Brno University of Technology, Faculty of Electronic Engineering and Communication
Místo
Brno
ISBN
978-80-214-6029-4
Kniha
Proceedings I of the 28th Conference STUDENT EEICT 2022 General papers
Edice
1
Strany od
247
Strany do
250
Strany počet
4
URL
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_1_v2.pdf
BibTex
@inproceedings{BUT188038, author="Jan {Vývoda} and Roman {Jakubíček}", title="Comparison of full-size and patches-based learning approaches for aneurysm segmentation in TOF-MRI data", booktitle="Proceedings I of the 28th Conference STUDENT EEICT 2022 General papers", year="2022", series="1", pages="247--250", publisher="Brno University of Technology, Faculty of Electronic Engineering and Communication", address="Brno", isbn="978-80-214-6029-4", url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_1_v2.pdf" }