Multi-atlas based neonatal brain extraction using atlas library clustering and local label fusion

2020 ◽  
Vol 79 (27-28) ◽  
pp. 19411-19433
Author(s):  
Negar Noorizadeh ◽  
Kamran Kazemi ◽  
Habibollah Danyali ◽  
Abbas Babajani-Feremi ◽  
Ardalan Aarabi
Keyword(s):  
Neonatal Brain ◽  
Label Fusion ◽  
Brain Extraction

Multi-atlas based neonatal brain extraction using a two-level patch-based label fusion strategy

2019 ◽  
Vol 54 ◽  
pp. 101602 ◽  
Author(s):  
Negar Noorizadeh ◽  
Kamran Kazemi ◽  
Habibollah Danyali ◽  
Ardalan Aarabi
Keyword(s):  
Neonatal Brain ◽  
Fusion Strategy ◽  
Label Fusion ◽  
Brain Extraction

scholarly journals A Multi‐Atlas Label Fusion Tool for Neonatal Brain MRI Parcellation and Quantification

2019 ◽  
Author(s):  
Yoshihisa Otsuka ◽  
Linda Chang ◽  
Yukako Kawasaki ◽  
Dan Wu ◽  
Can Ceritoglu ◽  
...  
Keyword(s):  
Brain Mri ◽  
Neonatal Brain ◽  
Label Fusion

scholarly journals Impacts of skull stripping on construction of three-dimensional T1-weighted imaging-based brain structural network in full-term neonates

2020 ◽  
Author(s):  
Geliang Wang ◽  
Yajie Hu ◽  
Xianjun Li ◽  
Miaomiao Wang ◽  
Congcong Liu ◽  
...  
Keyword(s):  
Brain Tissue ◽  
Full Term ◽  
Neonatal Brain ◽  
Tissue Segmentation ◽  
Term Neonates ◽  
Brain Extraction ◽  
Brain Tissue Segmentation ◽  
Skull Stripping ◽  
Structural Network ◽  
Manual Correction

Abstract Background: Skull stripping remains a challenge for neonatal brain MR image analysis. However, little is known about how accuracy of the skull stripping affects the neonatal brain tissue segmentation and subsequent network construction. This paper therefore aimed to clarify this issue by comparing two automatic (FSL’s Brain Extraction Tool, BET; Infant Brain Extraction and Analysis Toolbox, iBEAT) and a semiautomatic (iBEAT with manual correction) processes in constructing 3D T1-weighted imaging (T1WI)-based brain structural network. Methods: Twenty-two full-term neonates (gestational age, 37-42 weeks; boys/girls, 13/9) without abnormalities on MRI who underwent brain 3D T1WI were retrospectively recruited. Two automatic (BET and iBEAT) and a semiautomatic preprocessing (iBEAT with manual correction) workflows were separately used to perform the skull stripping. Brain tissue segmentation and volume calculation were performed by a John Hopkins atlas-based method. Sixty-four gray matter regions were selected as nodes; volume covariance network and its properties (clustering coefficient, C p ; characteristic path length, L p ; local efficiency, E local ; global efficiency, E global ) were calculated by GRETNA. Analysis of variance (ANOVA) was used to compare the differences in the calculated volumes between three workflows. Results: There were significant differences in volumes of 48 brain region between three workflows ( P <0.05). Three neonatal brain structural networks presented small-world topology. The semiautomatic workflow showed remarkably decreased C p , increased L p , decreased E local , and E global , in contrast to two automatic ones. Conclusions: Imperfect skull stripping indeed affected the accuracy of brain structural network in full-term neonates.

scholarly journals Impacts of skull stripping on construction of three-dimensional T1-weighted imaging-based brain structural network in full-term neonates

2020 ◽  
Author(s):  
Geliang Wang ◽  
Yajie Hu ◽  
Xianjun Li ◽  
Miaomiao Wang ◽  
Congcong Liu ◽  
...  
Keyword(s):  
Brain Tissue ◽  
Full Term ◽  
Neonatal Brain ◽  
Tissue Segmentation ◽  
Term Neonates ◽  
Brain Extraction ◽  
Brain Tissue Segmentation ◽  
Skull Stripping ◽  
Structural Network ◽  
Manual Correction

Abstract Background: Skull stripping remains a challenge for neonatal brain MR image analysis. However, little is known about how accuracy of the skull stripping affects the neonatal brain tissue segmentation and subsequent network construction. This paper therefore aimed to clarify this issue by comparing two automatic (FMRIB Software Library's Brain Extraction Tool, BET; Infant Brain Extraction and Analysis Toolbox, iBEAT) and a semiautomatic (iBEAT with manual correction) processes in constructing 3D T1-Weighted Imaging (T1WI)-based brain structural network. Methods: Twenty-two full-term neonates (gestational age, 37-42 weeks; boys/girls, 13/9) without abnormalities on MRI who underwent brain 3D T1WI were retrospectively recruited. Two automatic (BET and iBEAT) and a semiautomatic preprocessing (iBEAT with manual correction) workflows were separately used to perform the skull stripping. Brain tissue segmentation and volume calculation were performed by a John Hopkins atlas-based method. Sixty-four gray matter regions were selected as nodes; volume covariance network and its properties (clustering coefficient, C p ; characteristic path length, L p ; local efficiency, E local ; global efficiency, E global ) were calculated by GRETNA. Analysis of variance (ANOVA) was used to compare the differences in the calculated volume between three workflows. Results: There were significant differences in volumes of 50 brain regions between three workflows ( P <0.05). Three neonatal brain structural networks presented small-world topology. The semiautomatic workflow showed remarkably decreased C p , increased L p , decreased E local , and decreased E global , in contrast to two automatic ones. Conclusions: Imperfect skull stripping indeed affected the accuracy of brain structural network in full-term neonates.

scholarly journals Impacts of skull stripping on construction of three-dimensional T1-weighted imaging-based brain structural network in full-term neonates

2020 ◽  
Author(s):  
Geliang Wang ◽  
Yajie Hu ◽  
Xianjun Li ◽  
Miaomiao Wang ◽  
Congcong Liu ◽  
...  
Keyword(s):  
Brain Tissue ◽  
Full Term ◽  
Neonatal Brain ◽  
Tissue Segmentation ◽  
Term Neonates ◽  
Brain Extraction ◽  
Brain Tissue Segmentation ◽  
Skull Stripping ◽  
Structural Network ◽  
Manual Correction

Abstract Background: Skull stripping remains a challenge for neonatal brain MR image analysis. However, little is known about how accuracy of the skull stripping affects the neonatal brain tissue segmentation and subsequent network construction. This paper therefore aimed to clarify this issue by comparing two automatic (FMRIB Software Library's Brain Extraction Tool, BET; Infant Brain Extraction and Analysis Toolbox, iBEAT) and a semiautomatic (iBEAT with manual correction) processes in constructing 3D T1-Weighted Imaging (T1WI)-based brain structural network. Methods: Twenty-two full-term neonates (gestational age, 37-42 weeks; boys/girls, 13/9) without abnormalities on MRI who underwent brain 3D T1WI were retrospectively recruited. Two automatic (BET and iBEAT) and a semiautomatic preprocessing (iBEAT with manual correction) workflows were separately used to perform the skull stripping. Brain tissue segmentation and volume calculation were performed by a John Hopkins atlas-based method. Sixty-four gray matter regions were selected as nodes; volume covariance network and its properties (clustering coefficient, C p ; characteristic path length, L p ; local efficiency, E local ; global efficiency, E global ) were calculated by GRETNA. Analysis of variance (ANOVA) was used to compare the differences in the calculated volume between three workflows. Results: There were significant differences in volumes of 50 brain regions between three workflows ( P <0.05). Three neonatal brain structural networks presented small-world topology. The semiautomatic workflow showed remarkably decreased C p , increased L p , decreased E local , and decreased E global , in contrast to two automatic ones. Conclusions: Imperfect skull stripping indeed affected the accuracy of brain structural network in full-term neonates.

scholarly journals Accurate Learning with Few Atlases (ALFA): an algorithm for MRI neonatal brain extraction and comparison with 11 publicly available methods

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Ahmed Serag ◽  
Manuel Blesa ◽  
Emma J. Moore ◽  
Rozalia Pataky ◽  
Sarah A. Sparrow ◽  
...  
Keyword(s):  
Neonatal Brain ◽  
Brain Extraction

Hyperoxia and neonatal brain injury

2005 ◽  
Vol 36 (02) ◽  
Author(s):  
U Felderhoff-Mueser ◽  
AM Kaindl ◽  
C Bührer ◽  
H Ikonomidou
Keyword(s):  
Brain Injury ◽  
Neonatal Brain ◽  
Neonatal Brain Injury

HISTOLOGICAL INFLAMMATORY RESPONSES IN THE PLACENTA AND EARLY NEONATAL BRAIN INJURY

2006 ◽  
Vol preprint (2008) ◽  
pp. 1
Author(s):  
Vincenzo Zanardo ◽  
Stefania Vedovato ◽  
Agnese Suppiej ◽  
Daniele Trevisanuto ◽  
Mauro Migliore ◽  
...  
Keyword(s):  
Brain Injury ◽  
Inflammatory Responses ◽  
Neonatal Brain ◽  
Neonatal Brain Injury
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