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

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.

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

Brain Sciences ◽

10.3390/brainsci11060720 ◽

2021 ◽

Vol 11 (6) ◽

pp. 720

Author(s):

ZunHyan Rieu ◽

JeeYoung Kim ◽

Regina EY Kim ◽

Minho Lee ◽

Min Kyoung Lee ◽

...

Keyword(s):

White Matter ◽

Supervised Learning ◽

Brain Tissue ◽

Structural Changes ◽

Brain Aging ◽

High Reliability ◽

White Matter Hyperintensity ◽

Learning Method ◽

Similarity Coefficients ◽

Tissue Segmentation

White-matter hyperintensity (WMH) is a primary biomarker for small-vessel cerebrovascular disease, Alzheimer’s disease (AD), and others. The association of WMH with brain structural changes has also recently been reported. Although fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) provide valuable information about WMH, FLAIR does not provide other normal tissue information. The multi-modal analysis of FLAIR and T1-weighted (T1w) MRI is thus desirable for WMH-related brain aging studies. In clinical settings, however, FLAIR is often the only available modality. In this study, we thus propose a semi-supervised learning method for full brain segmentation using FLAIR. The results of our proposed method were compared with the reference labels, which were obtained by FreeSurfer segmentation on T1w MRI. The relative volume difference between the two sets of results shows that our proposed method has high reliability. We further evaluated our proposed WMH segmentation by comparing the Dice similarity coefficients of the reference and the results of our proposed method. We believe our semi-supervised learning method has a great potential for use for other MRI sequences and will encourage others to perform brain tissue segmentation using MRI modalities other than T1w.

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

American Journal of Neuroradiology ◽

10.3174/ajnr.a4262 ◽

2015 ◽

Vol 36 (6) ◽

pp. 1109-1115 ◽

Cited By ~ 9

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

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Hybrid Bee Colony and Cuckoo Search based centroid initialization for fuzzy c-means clustering in bio-medical image segmentation

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i8149.078919 ◽

2019 ◽

Vol 8 (9) ◽

pp. 1684-1688

Keyword(s):

Image Segmentation ◽

Brain Tissue ◽

Medical Image ◽

Cuckoo Search ◽

Medical Image Segmentation ◽

Tissue Segmentation ◽

Bee Colony ◽

Brain Tissue Segmentation ◽

Fuzzy C Means Clustering ◽

Number Of Iterations

In current years, the grouping has become well identified for numerous investigators due to several application fields like communication, wireless networking, and biomedical domain and so on. So, much different research has already been made by the investigators to progress an improved system for grouping. One of the familiar investigations is an optimization that has been efficiently applied for grouping. In this paper, propose a method of Hybrid Bee Colony and Cuckoo Search (HBCCS) based centroid initialization for fuzzy c-means clustering (FCM) in bio-medical image segmentation (HBCC-KFCM-BIM). For MRI brain tissue segmentation, KFCM is most preferable technique because of its performance. The major limitation of the conventional KFCM is random centroids initialization because it leads to rising the execution time to reach the best resolution. In order to accelerate the segmentation process, HBCCS is used to adjust the centroids of required clusters. The quantitative measures of results were compared using the metrics are the number of iterations and processing time. The number of iterations and processing of HBCC-KFCM-BIM method take minimum value while compared to conventional KFCM. The HBCC-KFCM-BIM method is very efficient and faster than conventional KFCM for brain tissue segmentation.

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White matter lesion extension to automatic brain tissue segmentation on MRI

NeuroImage ◽

10.1016/j.neuroimage.2009.01.011 ◽

2009 ◽

Vol 45 (4) ◽

pp. 1151-1161 ◽

Cited By ~ 173

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

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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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