Accuracy and reproducibility study of automatic MRI brain tissue segmentation methods

Boer, Renske de; Vrooman, Henri A.; Ikram, M. Arfan; Vernooij, Meike W.; Breteler, Monique M.B.; Lugt, Aad van der; Niessen, Wiro J.

doi:10.1016/j.neuroimage.2010.03.012

Cited by 122 publications

(116 citation statements)

References 40 publications

(53 reference statements)

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“…Briefly, as a marker of global atrophy, total brain volume was calculated as the sum of gray matter and white matter volumes of the five regions (frontal, parietal, occipital, temporal and central regions). WML volume was the summation of the WML in the above-mentioned five regions [27]. …”

Section: Methodsmentioning

confidence: 99%

Ankle-Brachial Index, Cognitive Impairment and Cerebrovascular Disease in a Chinese Population

Hilal¹,

Saini²,

Tan

et al. 2014

Neuroepidemiology

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Background: Previous studies have assessed the association between ankle-brachial index (ABI) and cognition, mainly using brief cognitive tests. We investigated whether ABI was associated with cognition independent of neuroimaging markers of cerebrovascular disease. Methods: Chinese subjects (n = 278, aged ≥60 years) were recruited from the ongoing Epidemiology of Dementia in Singapore (EDIS) Study. Ankle and brachial blood pressures were measured, and low ABI was defined as ≤0.9. A neuropsychological battery was utilized to determine cognition. Cognitive impairment no dementia (CIND) and dementia were diagnosed according to standard diagnostic criteria. Magnetic resonance imaging (MRI) was used to obtain semiquantitative and quantitative markers of cerebrovascular disease and atrophy. Results: A low ABI was related to the presence of intracranial stenosis (odds ratio, OR = 1.71; 95% confidence interval, CI: 1.13-2.59), but not with the presence of infarcts, microbleeds or grey matter, white matter and white matter lesion volumes. Furthermore, a low ABI was associated with poorer overall cognitive function and CIND-moderate/dementia (OR = 2.26; 95% CI: 1.11-4.59), independent of cardiovascular risk factors, and the MRI markers related to cerebrovascular disease and atrophy. Conclusion: We found an association between a low ABI and cognitive impairment, independent of any MRI marker of cerebral small vessel disease or large artery atherosclerotic disease.

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Section: Methodsmentioning

confidence: 99%

Ankle-Brachial Index, Cognitive Impairment and Cerebrovascular Disease in a Chinese Population

Hilal¹,

Saini²,

Tan

et al. 2014

Neuroepidemiology

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“…Intracranial volume (ICV), grey matter and white matter volumes were quantified by automatic segmentation at the Erasmus University Medical Center Rotterdam, The Netherlands [5,25]. Brain tissue segmentation was quantified by Proton density T1 and T2 weighted images.…”

Section: Neuroimagingmentioning

confidence: 99%

Retinal neurodegeneration on optical coherence tomography and cerebral atrophy

Ong

Hilal

Cheung

et al. 2015

Neuroscience Letters

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106

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“…Table 8 depicts the average value of the TPF, FPF, and SI of various classifiers. The proposed method showed high accuracy for all tissue classes and the SIs were close to the interobserver SI of the manual segmentations when compared to the state-of-the-art model using kNN classifier as given in [5]. However, for tissue types with less overlap, the SI measure shows a better distinction between the segmentation methods.…”

Section: Brain Tissue and Tumor Classification Using Elm With Rgsomentioning

confidence: 59%

A Hybrid DE-RGSO-ELM for Brain Tumor Tissue Categorization in 3D Magnetic Resonance Images

Kothavari

Devi

Deepa

2014

Mathematical Problems in Engineering

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Medical diagnostics, a technique used for visualizing the internal structures and functions of human body, serves as a scientific tool to assist physicians and involves direct use of digital imaging system analysis. In this scenario, identification of brain tumors is complex in the diagnostic process. Magnetic resonance imaging (MRI) technique is noted to best assist tissue contrast for anatomical details and also carries out mechanisms for investigating the brain by functional imaging in tumor predictions. Considering 3D MRI model, analyzing the anatomy features and tissue characteristics of brain tumor is complex in nature. Henceforth, in this work, feature extraction is carried out by computing 3D gray-level cooccurence matrix (3D GLCM) and run-length matrix (RLM) and feature subselection for dimensionality reduction is performed with basic differential evolution (DE) algorithm. Classification is performed using proposed extreme learning machine (ELM), with refined group search optimizer (RGSO) technique, to select the best parameters for better simplification and training of the classifier for brain tissue and tumor characterization as white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and tumor. Extreme learning machine outperforms the standard binary linear SVM and BPN for medical image classifier and proves better in classifying healthy and tumor tissues. The comparison between the algorithms proves that the mean and standard deviation produced by volumetric feature extraction analysis are higher than the other approaches. The proposed work is designed for pathological brain tumor classification and for 3D MRI tumor image segmentation. The proposed approaches are applied for real time datasets and benchmark datasets taken from dataset repositories.

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Accuracy and reproducibility study of automatic MRI brain tissue segmentation methods

Cited by 122 publications

References 40 publications

Ankle-Brachial Index, Cognitive Impairment and Cerebrovascular Disease in a Chinese Population

Ankle-Brachial Index, Cognitive Impairment and Cerebrovascular Disease in a Chinese Population

Retinal neurodegeneration on optical coherence tomography and cerebral atrophy

A Hybrid DE-RGSO-ELM for Brain Tumor Tissue Categorization in 3D Magnetic Resonance Images

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