Novel Method for Automatic Segmentation of Infiltrative Glioblastoma

Kelvin Wong; Stephen T. Wong

Novel Method for Automatic Segmentation of Infiltrative Glioblastoma

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

Abstract

Glioblastoma Multiforme (GBM) is the most lethal and common brain cancer in adult. Our goal is to quantitatively extract the infiltrating tumor information from imaging. Infiltrative tumor is with low Gd-enhancement and is difficult to identify. To investigate the prevalence and extent of low Gd-enhancement tumor in GBM, we developed an algorithm to automatically segment the low Gd-enhancement region. The method is applied to the GBM collection in The Cancer Imaging Archive (TCIA). The proposed algorithm can robustly segment different components of the tumor including low Gd-enhancement region.

Original language	English (US)
Title of host publication	Proceeding of International Society of Magnetic Resonance in Medicine
State	Published - 2015

Cite this

@inbook{6d3a226fd6a44cf3a7aedf46c4d3e45e,

title = "Novel Method for Automatic Segmentation of Infiltrative Glioblastoma",

abstract = "Glioblastoma Multiforme (GBM) is the most lethal and common brain cancer in adult. Our goal is to quantitatively extract the infiltrating tumor information from imaging. Infiltrative tumor is with low Gd-enhancement and is difficult to identify. To investigate the prevalence and extent of low Gd-enhancement tumor in GBM, we developed an algorithm to automatically segment the low Gd-enhancement region. The method is applied to the GBM collection in The Cancer Imaging Archive (TCIA). The proposed algorithm can robustly segment different components of the tumor including low Gd-enhancement region. ",

author = "Kelvin Wong and Wong, {Stephen T.}",

year = "2015",

language = "English (US)",

booktitle = "Proceeding of International Society of Magnetic Resonance in Medicine",

}

TY - CHAP

T1 - Novel Method for Automatic Segmentation of Infiltrative Glioblastoma

AU - Wong, Kelvin

AU - Wong, Stephen T.

PY - 2015

Y1 - 2015

N2 - Glioblastoma Multiforme (GBM) is the most lethal and common brain cancer in adult. Our goal is to quantitatively extract the infiltrating tumor information from imaging. Infiltrative tumor is with low Gd-enhancement and is difficult to identify. To investigate the prevalence and extent of low Gd-enhancement tumor in GBM, we developed an algorithm to automatically segment the low Gd-enhancement region. The method is applied to the GBM collection in The Cancer Imaging Archive (TCIA). The proposed algorithm can robustly segment different components of the tumor including low Gd-enhancement region.

AB - Glioblastoma Multiforme (GBM) is the most lethal and common brain cancer in adult. Our goal is to quantitatively extract the infiltrating tumor information from imaging. Infiltrative tumor is with low Gd-enhancement and is difficult to identify. To investigate the prevalence and extent of low Gd-enhancement tumor in GBM, we developed an algorithm to automatically segment the low Gd-enhancement region. The method is applied to the GBM collection in The Cancer Imaging Archive (TCIA). The proposed algorithm can robustly segment different components of the tumor including low Gd-enhancement region.

M3 - Chapter (peer-reviewed)

BT - Proceeding of International Society of Magnetic Resonance in Medicine

ER -

Novel Method for Automatic Segmentation of Infiltrative Glioblastoma

Abstract

Fingerprint

Cite this