scholarly journals Evaluating the Effects of White Matter Multiple Sclerosis Lesions on the Volume Estimation of 6 Brain Tissue Segmentation Methods

2015 ◽  
Vol 36 (6) ◽  
pp. 1109-1115 ◽  
Author(s):  
S. Valverde ◽  
A. Oliver ◽  
Y. Díez ◽  
M. Cabezas ◽  
J.C. Vilanova ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Brain Tissue ◽  
Volume Estimation ◽  
Tissue Segmentation ◽  
Brain Tissue Segmentation ◽  
Segmentation Methods

White matter lesion extension to automatic brain tissue segmentation on MRI

NeuroImage ◽  
2009 ◽  
Vol 45 (4) ◽  
pp. 1151-1161 ◽  
Author(s):  
Renske de Boer ◽  
Henri A. Vrooman ◽  
Fedde van der Lijn ◽  
Meike W. Vernooij ◽  
M. Arfan Ikram ◽  
...  
Keyword(s):  
White Matter ◽  
Brain Tissue ◽  
White Matter Lesion ◽  
Tissue Segmentation ◽  
Brain Tissue Segmentation ◽  
Lesion Extension

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

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.

scholarly journals Semi-Supervised Learning in Medical Image Segmentation: Brain Tissue with White Matter Hyperintensity Segmentation Using FLAIR Image

2021 ◽  
Author(s):  
ZunHyan Rieu ◽  
Donghyeon Kim ◽  
JeeYoung Kim ◽  
Regina EY Kim ◽  
Minho Lee ◽  
...  
Keyword(s):  
White Matter ◽  
Supervised Learning ◽  
Brain Tissue ◽  
Medical Image Segmentation ◽  
White Matter Hyperintensity ◽  
Learning Method ◽  
Brain Segmentation ◽  
Tissue Segmentation ◽  
Flair Image ◽  
Brain Tissue Segmentation

White matter hyperintensity (WMH) has been considered the primary biomarker from small-vessel cerebrovascular disease to Alzheimer’s disease (AD) and has been reported for its correlation of brain structural changes. To perform WMH related analysis with brain structure, both T1-weighted (T1w) and (Fluid Attenuated Inversion Recovery(FLAIR) are required. However, in a clinical situation, it is limited to obtain 3D T1w and FLAIR images simultaneously. Also, the most of brain segmentation technique supports 3D T1w only. Therefore, we introduced the semi-supervised learning method that can perform brain segmentation using FLAIR image only. Our method achieved a dice overlap score of 0.86 for brain tissue segmentation on FLAIR, with the relative volume difference between T1w and FLAIR segmentation under 4.8%, which is just as reliable as the segmentation done by its paired T1w image. We believe our semi-supervised learning method has a great potential to be used to other MRI sequences and provide encouragement to people who seek brain tissue segmentation from a non-T1w image.

Comparison of 10 brain tissue segmentation methods using revisited IBSR annotations

2014 ◽  
Vol 41 (1) ◽  
pp. 93-101 ◽  
Author(s):  
Sergi Valverde ◽  
Arnau Oliver ◽  
Mariano Cabezas ◽  
Eloy Roura ◽  
Xavier Lladó
Keyword(s):  
Brain Tissue ◽  
Tissue Segmentation ◽  
Brain Tissue Segmentation ◽  
Segmentation Methods

Accuracy and reproducibility study of automatic MRI brain tissue segmentation methods

NeuroImage ◽  
2010 ◽  
Vol 51 (3) ◽  
pp. 1047-1056 ◽  
Author(s):  
Renske de Boer ◽  
Henri A. Vrooman ◽  
M. Arfan Ikram ◽  
Meike W. Vernooij ◽  
Monique M.B. Breteler ◽  
...  
Keyword(s):  
Brain Tissue ◽  
Tissue Segmentation ◽  
Mri Brain ◽  
Brain Tissue Segmentation ◽  
Reproducibility Study ◽  
Segmentation Methods

GSCFN: A Graph Self-Construction and Fusion Network for Semi-supervised Brain Tissue Segmentation in MRI

2021 ◽  
Author(s):  
Yan Zhang ◽  
Yifei Li ◽  
Youyong Kong ◽  
Jiasong Wu ◽  
Jian Yang ◽  
...  
Keyword(s):  
Brain Tissue ◽  
Tissue Segmentation ◽  
Brain Tissue Segmentation
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