Automatic segmentation of White Matter Hyperintensities: validation and comparison with state-of-the-art methods on both Multiple Sclerosis and elderly subjects

A system for the automatic classification of cardiac sounds can be of great help for doctors in the diagnosis of cardiac diseases. Generally speaking, the main stages of such systems are (i) the pre-processing of the heart sound signal, (ii) the segmentation of the cardiac cycles, (iii) feature extraction and (iv) classification. In this paper, we propose methods for each of these stages. The modified empirical wavelet transform (EWT) and the normalized Shannon average energy are used in pre-processing and automatic segmentation to identify the systolic and diastolic intervals in a heart sound recording; then, six power characteristics are extracted (three for the systole and three for the diastole)—the motivation behind using power features is to achieve a low computational cost to facilitate eventual real-time implementations. Finally, different models of machine learning (support vector machine (SVM), k-nearest neighbor (KNN), random forest and multilayer perceptron) are used to determine the classifier with the best performance. The automatic segmentation method was tested with the heart sounds from the Pascal Challenge database. The results indicated an error (computed as the sum of the differences between manual segmentation labels from the database and the segmentation labels obtained by the proposed algorithm) of 843,440.8 for dataset A and 17,074.1 for dataset B, which are better values than those reported with the state-of-the-art methods. For automatic classification, 805 sample recordings from different databases were used. The best accuracy result was 99.26% using the KNN classifier, with a specificity of 100% and a sensitivity of 98.57%. These results compare favorably with similar works using the state-of-the-art methods.

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The Evolution of White Matter Hyperintensities

US Neurology ◽

10.17925/usn.2010.05.02.10 ◽

2010 ◽

Vol 05 (02) ◽

pp. 10 ◽

Cited By ~ 1

Author(s):

Vanessa G Young ◽

Jillian J Kril ◽

◽

Keyword(s):

White Matter ◽

Blood Brain Barrier ◽

White Matter Hyperintensities ◽

Brain Barrier ◽

Common Finding ◽

Elderly Subjects ◽

Vascular Integrity ◽

Blood Brain ◽

Mri Scans ◽

Mri Changes

White matter hyperintensities (WMHs) are a common finding on magnetic resonance imaging (MRI) scans of elderly subjects. Despite their frequency, the clinical correlates and etiology of WMH remain controversial, with many conflicting results published. This is due, in part, to the varied populations studied. Nevertheless, the prevailing opinion is that these lesions are of vascular origin due to the strong associations with vascular risk factors and stroke. Neuropathological studies have also yielded varied results. Interestingly, while a number of associations with variables such as demyelination and gliosis have been reported, no single pathological variable has been found to account for the MRI changes. The most consistent associations are with reduced vascular integrity and increased bloodbrain barrier permeability. Further studies investigating the bloodbrain barrier may assist in elucidating the origin of these common abnormalities.

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Volumetric comparisons of supratentorial white matter hyperintensities on FLAIR MRI in patients with migraine and multiple sclerosis

Journal of Clinical Neuroscience ◽

10.1016/j.jocn.2011.07.044 ◽

2012 ◽

Vol 19 (5) ◽

pp. 696-701 ◽

Cited By ~ 9

Author(s):

David Olayinka Kamson ◽

Zsolt Illés ◽

Mihály Aradi ◽

Gergely Orsi ◽

Gábor Perlaki ◽

...

Keyword(s):

Multiple Sclerosis ◽

White Matter ◽

White Matter Hyperintensities ◽

Supratentorial White Matter

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IC-P-139: Accurate automatic segmentation of white matter hyperintensities using a linear regression classifier

Alzheimer s & Dementia ◽

10.1016/j.jalz.2015.06.161 ◽

2015 ◽

Vol 11 (7S_Part_2) ◽

pp. P94-P95

Author(s):

Mahsa Dadar ◽

Tharick Ali Pascoal ◽

Sarinporn Manitsirikul ◽

John Breitner ◽

Pedro Rosa-Neto ◽

...

Keyword(s):

White Matter ◽

Linear Regression ◽

White Matter Hyperintensities ◽

Automatic Segmentation

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Segmentation of Cerebral Small Vessel Diseases-White Matter Hyperintensities Based on a Deep Learning System

Frontiers in Medicine ◽

10.3389/fmed.2021.681183 ◽

2021 ◽

Vol 8 ◽

Author(s):

Wei Shan ◽

Yunyun Duan ◽

Yu Zheng ◽

Zhenzhou Wu ◽

Shang Wei Chan ◽

...

Keyword(s):

Deep Learning ◽

White Matter ◽

White Matter Hyperintensities ◽

Automatic Segmentation ◽

Learning System ◽

Test Set ◽

Minor Revision ◽

Lesion Level ◽

External Test ◽

Cerebral Small Vessel Diseases

Objective: Reliable quantification of white matter hyperintensities (WHMs) resulting from cerebral small vessel diseases (CSVD) is essential for understanding their clinical impact. We aim to develop and clinically validate a deep learning system for automatic segmentation of CSVD-WMH from fluid-attenuated inversion recovery (FLAIR) imaging using large multicenter data.Method: A FLAIR imaging dataset of 1,156 patients diagnosed with CSVD associated WMH (median age, 54 years; 653 males) obtained between September 2018 and September 2019 from Beijing Tiantan Hospital was retrospectively analyzed in this study. Locations of CSVD-WMH on the FLAIR scans were manually marked by two experienced neurologists. Using the manually labeled data of 996 patients (development set), a U-shaped novel 2D convolutional neural network (CNN) architecture was trained for automatic segmentation of CSVD-WMH. The segmentation performance of the network was evaluated with per pixel and lesion level dice scores using an independent internal test set (n = 160) and a multi-center external test set (n = 90, three medical centers). The clinical suitability of the segmentation results, classified as acceptable, acceptable with minor revision, acceptable with major revision, and not acceptable, was analyzed by three independent neuroradiologists. The inter-neuroradiologists agreement rate was assessed by the Kendall-W test.Results: On the internal and external test sets, the proposed CNN architecture achieved per pixel and lesion level dice scores of 0.72 (external test set), and they were significantly better than the state-of-the-art deep learning architectures proposed for WMH segmentation. In the clinical evaluation, neuroradiologists observed the segmentation results for 95% of the patients were acceptable or acceptable with a minor revision.Conclusions: A deep learning system can be used for automated, objective, and clinically meaningful segmentation of CSVD-WMH with high accuracy.

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Investigating the Contribution of White Matter Hyperintensities and Cortical Thickness to Empathy in Neurodegenerative and Cerebrovascular Diseases

10.1101/2021.08.01.454640 ◽

2021 ◽

Author(s):

Miracle Ozzoude ◽

Brenda Varriano ◽

Derek Beaton ◽

Joel Ramirez ◽

Melissa F Holmes ◽

...

Keyword(s):

White Matter ◽

Cortical Thickness ◽

Brain Atrophy ◽

White Matter Hyperintensities ◽

Automatic Segmentation ◽

Cerebrovascular Diseases ◽

Cortical Atrophy ◽

Reactivity Index ◽

Empathy Deficits ◽

Longitudinal Effects

Introduction: Change in empathy is an increasingly recognised symptom of neurodegenerative diseases and contributes to caregiver burden and patient distress. Empathy impairment has been associated with brain atrophy but its relationship to white matter hyperintensities (WMH) is unknown. We aimed to investigate the relationships amongst WMH, brain atrophy, and empathy deficits in neurodegenerative and cerebrovascular diseases. Methods: 513 participants with Alzheimers Disease/Mild Cognitive Impairment, Amyotrophic Lateral Sclerosis, Frontotemporal Dementia (FTD), Parkinsons Disease, or Cerebrovascular Disease (CVD) were included. Empathy was assessed using the Interpersonal Reactivity Index. WMH were measured using a semi-automatic segmentation and FreeSurfer was used to measure cortical thickness. Results: A heterogeneous pattern of cortical thinning was found between groups, with FTD showing thinning in frontotemporal regions and CVD in left superior parietal, left insula, and left postcentral. Results from both univariate and multivariate analyses revealed that several variables were associated with empathy, particularly cortical thickness in the fronto-insulo-temporal and cingulate regions, sex(female), global cognition, and right parietal and occipital WMH. Conclusions: Our results suggest that cortical atrophy and WMH may be associated with empathy deficits in neurodegenerative and cerebrovascular diseases. Future work should consider investigating the longitudinal effects of WMH and atrophy on empathy deficits in neurodegenerative and cerebrovascular diseases.

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Cerebral White Matter Hyperintensities in T2 Weighted Magnetic Resonance Images of Elderly Patients- Correlation with their Mental Status

Journal of BP Koirala Institute of Health Sciences ◽

10.3126/jbpkihs.v4i1.36107 ◽

2021 ◽

Vol 4 (1) ◽

pp. 26-31

Author(s):

Niraj Regmi ◽

Abu Saleh Mohiuddin ◽

Abu Taher ◽

Mahfuz Ara Ferdousi

Keyword(s):

White Matter ◽

Magnetic Resonance ◽

Elderly Patients ◽

Mental Status ◽

Negative Correlation ◽

White Matter Hyperintensities ◽

Mmse Score ◽

Significant Negative Correlation ◽

Magnetic Resonance Images ◽

Elderly Subjects

Background: White matter hyperintensities (WMH), focal and/or diffuse areas of hyperintense signals on T2 weighted magnetic resonance imaging (MRI), are the most common incidental finding in elderly patients. However, their clinical significance is usually overlooked. We aimed to find out the correlation between the degree of cerebral WMH in MRI with the mental status of elderly patients, assessed by Mini-Mental Status Examination (MMSE) score. Methods: This cross-sectional study was conducted for two years on eighty eligible elderly patients (> 60 years) referred to the Department of Radiology and Imaging for MRI of the brain. Demographic variables like age and sex, MMSE score, and MRI variables like location and number of WMHs were studied. The Pearson’s correlation coefficient was used to calculate the correlation between the extent of periventricular WMHs and MMSE score. Results: A significant negative correlation (r = -0.78; p < 0.001) was found between decreased MMSE and the extent of periventricular WMH. A significant negative correlation was also found when periventricular hyperintensities were evaluated individually for frontal caps (r = -0.68; p < 0.0001), band opacities (r = -0.55; p<0.0001) and occipital cap (r = -0.59; p < 0.0001). However, subcortical WMH was not significantly corelated with MMSE score (r = +0.018, p = 0.0897). Conclusion: A significant negative correlation exists between the extent of periventricular WMH seen at brain MRI with cognitive decline in elderly subjects. However, no such correlation exists between subcortical WMH and mental status.

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White-matter-nulled MPRAGE at 7T reveals thalamic lesions and atrophy of specific thalamic nuclei in multiple sclerosis

Multiple Sclerosis Journal ◽

10.1177/1352458519828297 ◽

2019 ◽

Vol 26 (8) ◽

pp. 987-992 ◽

Cited By ~ 3

Author(s):

Vincent Planche ◽

Jason H Su ◽

Sandy Mournet ◽

Manojkumar Saranathan ◽

Vincent Dousset ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Multiple Sclerosis ◽

White Matter ◽

Automatic Segmentation ◽

Resonance Imaging ◽

Thalamic Nuclei ◽

Individual Sequences ◽

Weighted Magnetic Resonance Imaging ◽

Thalamic Lesions ◽

Magnetic Resonance Imaging Mri

Background: Investigating the degeneration of specific thalamic nuclei in multiple sclerosis (MS) remains challenging. Methods: White-matter-nulled (WMn) MPRAGE, MP-FLAIR, and standard T1-weighted magnetic resonance imaging (MRI) were performed on MS patients ( n = 15) and matched controls ( n = 12). Thalamic lesions were counted in individual sequences and lesion contrast-to-noise ratio (CNR) was measured. Volumes of 12 thalamic nuclei were measured using an automatic segmentation pipeline specifically developed for WMn-MPRAGE. Results: WMn-MPRAGE showed more thalamic MS lesions ( n = 35 in 9 out of 15 patients) than MP-FLAIR ( n = 25) and standard T1 ( n = 23), which was associated with significant improvement of CNR ( p < 0.0001). MS patients had whole thalamus atrophy ( p = 0.003) with lower volumes found for the anteroventral ( p < 0.001), the pulvinar ( p < 0.0001), and the habenular ( p = 0.004) nuclei. Conclusion: WMn-MPRAGE and automatic thalamic segmentation can highlight thalamic MS lesions and measure patterns of focal thalamic atrophy.

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