Semi-automated segmentation of pre-operative low grade gliomas in magnetic resonance imaging

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11110%2F15%3A10297781" target="_blank" >RIV/00216208:11110/15:10297781 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61383082:_____/15:#0000361 RIV/00159816:_____/15:00063200

Výsledek na webu

<a href="http://dx.doi.org/10.1186/s40644-015-0047-z" target="_blank" >http://dx.doi.org/10.1186/s40644-015-0047-z</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s40644-015-0047-z" target="_blank" >10.1186/s40644-015-0047-z</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Semi-automated segmentation of pre-operative low grade gliomas in magnetic resonance imaging

Popis výsledku v původním jazyce

Segmentation of pre-operative low-grade gliomas (LGGs) from magnetic resonance imaging is a crucial step for studying imaging biomarkers. However, segmentation of LGGs is particularly challenging because they rarely enhance after gadolinium administration. Like other gliomas, they have irregular tumor shape, heterogeneous composition, ill-defined tumor boundaries, and limited number of image types. To overcome these challenges we propose a semi-automated segmentation method that relies only on T2-weighted (T2W) and optionally post-contrast T1-weighted (T1W) images. First, the user draws a region-of-interest (ROI) that completely encloses the tumor and some normal tissue. Second, a normal brain atlas and post-contrast T1W images are registered to T2W images. Third, the posterior probability of each pixel/voxel belonging to normal and abnormal tissues is calculated based on information derived from the atlas and ROI. Finally, geodesic active contours use the probability map of the tumor

Název v anglickém jazyce

Semi-automated segmentation of pre-operative low grade gliomas in magnetic resonance imaging

Popis výsledku anglicky

Segmentation of pre-operative low-grade gliomas (LGGs) from magnetic resonance imaging is a crucial step for studying imaging biomarkers. However, segmentation of LGGs is particularly challenging because they rarely enhance after gadolinium administration. Like other gliomas, they have irregular tumor shape, heterogeneous composition, ill-defined tumor boundaries, and limited number of image types. To overcome these challenges we propose a semi-automated segmentation method that relies only on T2-weighted (T2W) and optionally post-contrast T1-weighted (T1W) images. First, the user draws a region-of-interest (ROI) that completely encloses the tumor and some normal tissue. Second, a normal brain atlas and post-contrast T1W images are registered to T2W images. Third, the posterior probability of each pixel/voxel belonging to normal and abnormal tissues is calculated based on information derived from the atlas and ROI. Finally, geodesic active contours use the probability map of the tumor

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

FD - Onkologie a hematologie

OECD FORD obor

—

Projekt

<a href="/cs/project/ED1.100%2F02%2F0123" target="_blank" >ED1.100/02/0123: Fakultní nemocnice u sv. Anny v Brně - Mezinárodní centrum klinického výzkumu (FNUSA - ICRC)</a><br>

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Cancer Imaging

ISSN

1470-7330

e-ISSN

—

Svazek periodika

15

Číslo periodika v rámci svazku

August

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

10

Strana od-do

1-10

Kód UT WoS článku

000359640700001

EID výsledku v databázi Scopus

2-s2.0-84938915776