Sub-region segmentation of brain tumors from multimodal MRI images using 3D U-Net

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F23%3A63570398" target="_blank" >RIV/70883521:28140/23:63570398 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-21438-7_29" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-21438-7_29</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-21438-7_29" target="_blank" >10.1007/978-3-031-21438-7_29</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sub-region segmentation of brain tumors from multimodal MRI images using 3D U-Net

Popis výsledku v původním jazyce

Accurate segmentation of brain tumors from the magnetic resonance image (MRI) is an essential step for radionics analysis as well as finding the tumor extension is so necessary to plan the best treatment to improve the survival rate. Manually extracting sub-regions of the brain tumor from MRI is a tedious process and time-consuming, as the complex brain tumor images require extensive human expertise. In recent years, deep learning models have proved effective in medical image segmentation tasks. In brain tumor segmentation, the 3D multimodal MRI poses some challenges such as computation and memory limitations. This study aims to develop a deep learning model using 3D U-Net for brain tumor segmentation. The segmentation results on BraTS 2020 dataset show that the proposed model achieves promising performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Název v anglickém jazyce

Sub-region segmentation of brain tumors from multimodal MRI images using 3D U-Net

Popis výsledku anglicky

Accurate segmentation of brain tumors from the magnetic resonance image (MRI) is an essential step for radionics analysis as well as finding the tumor extension is so necessary to plan the best treatment to improve the survival rate. Manually extracting sub-regions of the brain tumor from MRI is a tedious process and time-consuming, as the complex brain tumor images require extensive human expertise. In recent years, deep learning models have proved effective in medical image segmentation tasks. In brain tumor segmentation, the 3D multimodal MRI poses some challenges such as computation and memory limitations. This study aims to develop a deep learning model using 3D U-Net for brain tumor segmentation. The segmentation results on BraTS 2020 dataset show that the proposed model achieves promising performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Networks and System (Volume 597 LNNS)

ISBN

978-3-031-21437-0

ISSN

2367-3370

e-ISSN

—

Počet stran výsledku

11

Strana od-do

357-367

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

Berlín

Místo konání akce

on-line

Datum konání akce

10. 10. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000992418500029