Detail publikace

Comparison of full-size and patches-based learning approaches for aneurysm segmentation in TOF-MRI data

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"
}