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

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.

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

10.21203/rs.2.22268/v3 ◽

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.

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

10.21203/rs.2.22268/v2 ◽

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.

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Automatic neonatal brain tissue segmentation with MRI

10.1117/12.2006653 ◽

2013 ◽

Cited By ~ 12

Author(s):

Vedran Srhoj-Egekher ◽

Manon J. N. L. Benders ◽

Max A. Viergever ◽

Ivana Išgum

Keyword(s):

Brain Tissue ◽

Neonatal Brain ◽

Tissue Segmentation ◽

Brain Tissue Segmentation

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Auto-kNN: Brain Tissue Segmentation using Automatically Trained k-Nearest-Neighbor Classification

10.54294/c31lhj ◽

2013 ◽

Author(s):

Henri Vrooman ◽

Fedde Van der Lijn ◽

Wiro Niessen

Keyword(s):

White Matter ◽

Brain Tissue ◽

Nearest Neighbor ◽

White Matter Lesion ◽

Lesion Detection ◽

K Nearest Neighbor ◽

Tissue Segmentation ◽

Rigid Registration ◽

Brain Tissue Segmentation ◽

Skull Stripping

In this paper we applied one of our regularly used processing pipelines for fully automated brain tissue segmentation. Brain tissue was segmented in cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM). Our algorithms for skull stripping, tissue segmentation and white matter lesion (WML) detection were slightly adapted and applied to twelve data sets within the MRBrainS13 brain tissue segmentation challenge. Skull stripping is performed using non-rigid registration of 5 atlas masks. Our tissue segmentation is based on an automatically trained kNN-classifier. Training samples were obtained by non-rigid registration of 5 manually labeled scans followed by a pruning step in feature space to remove any residual erroneously sampled tissue voxels. The kNN-classification incorporates voxel intensities from a T1-weighted scan and a FLAIR scan. The white matter lesion detection is based on an automatically determined threshold on the FLAIR scan. The application of the algorithms on the data from the MRBrainS13 Challenge showed that our pipeline produces acceptable segmentations. Average resulting Dice scores were 77.86 (CSF), 81.22 (GM), 87.27 (WM), 93.78 (total parenchyma), and 96.26 (all intracranial structures). Total processing time was about 2 hours per subject.

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