White Matter Hyperintensities Segmentation Research Articles

Longitudinal segmentation of age-related white matter hyperintensities.

Although white matter hyperintensities evolve in the course of ageing, few solutions exist to consider the lesion segmentation problem longitudinally. Based on an existing automatic lesion segmentation algorithm, a longitudinal extension is proposed. For evaluation purposes, a longitudinal lesion simulator is created allowing for the comparison between the longitudinal and the cross-sectional version in various situations of lesion load progression. Finally, applied to clinical data, the proposed framework demonstrates an increased robustness compared to available cross-sectional methods and findings are aligned with previously reported clinical patterns.

APOE ε4 status is associated with white matter hyperintensities volume accumulation rate independent of AD diagnosis

To assess the relationship between carriage of APOE ε4 allele and evolution of white matter hyperintensities (WMHs) volume, we longitudinally studied 339 subjects from the Alzheimer's Disease Neuroimaging Initiative cohort with diagnoses ranging from normal controls to probable Alzheimer's disease (AD). A purpose-built longitudinal automatic method was used to segment WMH using constraints derived from an atlas-based model selection applied to a time-averaged image. Linear mixed models were used to evaluate the differences in rate of change across diagnosis and genetic groups. After adjustment for covariates (age, sex, and total intracranial volume), homozygous APOE ε4ε4 subjects had a significantly higher rate of WMH accumulation (22.5% per year 95% CI [14.4, 31.2] for a standardized population having typical values of covariates) compared with the heterozygous (ε4ε3) subjects (10.0% per year [6.7, 13.4]) and homozygous ε3ε3 (6.6% per year [4.1, 9.3]) subjects. Rates of accumulation increased with diagnostic severity; controls accumulated 5.8% per year 95% CI: [2.2, 9.6] for the standardized population, early mild cognitive impairment 6.6% per year [3.9, 9.4], late mild cognitive impairment 12.5% per year [8.2, 17.0] and AD subjects 14.7% per year [6.0, 24.0]. Following adjustment for APOE status, these differences became nonstatistically significant suggesting that APOE ε4 genotype is the major driver of accumulation of WMH volume rather than diagnosis of AD.

BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities

Reliable quantification of white matter hyperintensities of presumed vascular origin (WMHs) is increasingly needed, given the presence of these MRI findings in patients with several neurological and vascular disorders, as well as in elderly healthy subjects.We present BIANCA (Brain Intensity AbNormality Classification Algorithm), a fully automated, supervised method for WMH detection, based on the k-nearest neighbour (k-NN) algorithm. Relative to previous k-NN based segmentation methods, BIANCA offers different options for weighting the spatial information, local spatial intensity averaging, and different options for the choice of the number and location of the training points. BIANCA is multimodal and highly flexible so that the user can adapt the tool to their protocol and specific needs.We optimised and validated BIANCA on two datasets with different MRI protocols and patient populations (a “predominantly neurodegenerative” and a “predominantly vascular” cohort).BIANCA was first optimised on a subset of images for each dataset in terms of overlap and volumetric agreement with a manually segmented WMH mask. The correlation between the volumes extracted with BIANCA (using the optimised set of options), the volumes extracted from the manual masks and visual ratings showed that BIANCA is a valid alternative to manual segmentation. The optimised set of options was then applied to the whole cohorts and the resulting WMH volume estimates showed good correlations with visual ratings and with age. Finally, we performed a reproducibility test, to evaluate the robustness of BIANCA, and compared BIANCA performance against existing methods.Our findings suggest that BIANCA, which will be freely available as part of the FSL package, is a reliable method for automated WMH segmentation in large cross-sectional cohort studies.

Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review.

White matter hyperintensities (WMH) are commonly seen in the brain of healthy elderly subjects and patients with several neurological and vascular disorders. A truly reliable and fully automated method for quantitative assessment of WMH on magnetic resonance imaging (MRI) has not yet been identified. In this paper, we review and compare the large number of automated approaches proposed for segmentation of WMH in the elderly and in patients with vascular risk factors. We conclude that, in order to avoid artifacts and exclude the several sources of bias that may influence the analysis, an optimal method should comprise a careful preprocessing of the images, be based on multimodal, complementary data, take into account spatial information about the lesions and correct for false positives. All these features should not exclude computational leanness and adaptability to available data.Electronic supplementary materialThe online version of this article (doi:10.1007/s12021-015-9260-y) contains supplementary material, which is available to authorized users.

Automatic Segmentation and Quantitative Analysis of White Matter Hyperintensities on FLAIR Images Using Trimmed-Likelihood Estimator

Quantitative analysis of white matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images provides information for disease tracking, therapeutic efficacy assessment, and cognitive science research. This study developed an automatic segmentation method to detect and quantify WMHs on FLAIR images. This study aims to assess the accuracy and reproducibility of the proposed method. The FLAIR images of 82 patients (58-84years) with different WMH burdens were acquired with the same 3T scanner (Intera-achieva SMI-2.1; Philip Medical System, Sixth Affiliated People's Hospital, Shanghai, China). The FLAIR images were preprocessed through brain extraction and intensity inhomogeneity correction. An anatomy atlas built from a set of 20 patients with different WMH burdens (mild, 11 patients; moderate, 6 patients; and severe, 3 patients) was used to estimate a control parameter in the framework of segmentation. The general flow for WMH segmentation included classification of foreground and background regions, detection of abnormally high signals, and WMH refinement. The performance of automatic segmentation was evaluated by a volumetric comparison with manual segmentation on patients with different WMH burdens. Similarity index values for the accuracy of automatic segmentation compared to manual segmentation were consistently high for patients with different WMH burdens (mild, 0.78±0.08; moderate, 0.83±0.06; severe, 0.84±0.08; and total, 0.80±0.08). Linear regression demonstrated a strong correlation between the WMH volumes measured by the two methods in all patients (r=0.98, P=.006). Small average differences were detected between the WMH volumes obtained through manual and automatic segmentations (mild, 4.76%; moderate, 6.84%; and severe, 7.59%). The results of Bland-Altman analysis show a system bias of 0.68cm(3) and a standard deviation of 2.10cm(3) over the range of 2.58-53.9cm(3). The developed method is accurate and efficient in detecting and quantifying differently sized WMHs on FLAIR images. Automatic segmentation is a promising computer-aided diagnostic tool to study WMHs in aging and dementia in basic research and even in clinical trials.

Automated segmentation and quantification of white matter hyperintensities in acute ischemic stroke patients with cerebral infarction.

White matter hyperintensities (WMHs) of presumed vascular origin are common in ageing population, especially in patients with acute cerebral infarction and the volume has been reported to be associated with mental impairment and the risk of hemorrhage from antithrombotic agents. WMHs delineation can be computerized to minimize human bias. However, the presence of cerebral infarcts greatly degrades the accuracy of WMHs detection and thus limits the application of computerized delineation to patients with acute cerebral infarction. We propose a computer-assisted segmentation method to depict WMHs in the presence of cerebral infarcts in combined T1-weighted, fluid attenuation inversion recovery, and diffusion-weighted magnetic resonance imaging (MRI). The proposed method detects WMHs by empirical threshold and atlas information, with subtraction of white matter voxels affected by acute infarction. The method was derived using MRI from 25 hemispheres with WMHs only and 13 hemispheres with both WMHs and cerebral infarcts. Similarity index (SI) and correlation were utilized to assess the agreement between the new automated method and a gold standard visually guided semi-automated method done by an expert rater. The proposed WMHs segmentation approach produced average SI, sensitivity and specificity of 83.142±11.742, 84.154±16.086 and 99.988±0.029% with WMHs only and of 68.826±14.036, 74.381±18.473 and 99.956±0.054% with both WMHs and cerebral infarcts in the derivation cohort. The performance of the proposed method with an external validation cohort was also highly consistent with that of the experienced rater.

White matter hyper-intensities automatic identification and segmentation in magnetic resonance images

A methodology for automatic identification and segmentation of white matter hyper-intensities appearing in magnetic resonance images of brain axial cuts is presented. To this end, a sequence of image processing technics is employed to form an image where the hyper-intensities in white matter differ notoriously from the rest of the objects. This pre-processing stage facilitates the posterior process of identification and segmentation of the hyper-intensity volumes. The proposed methodology was tested on 55 magnetic resonance images from six patients. These images were analysed by the proposed system and the resulted hyper-intensity images were compared with the images manually segmented by experts. The experimental results show the mean rate of true positives of 0.9, the mean rate of false positives of 0.7 and the similarity index of 0.7; it is worth commenting that the false positives are found mostly within the grey matter not causing problems in early diagnosis. The proposed methodology for magnetic resonance image processing and analysis may be useful in the early detection of white matter lesions.

Spotlight on the June 3 Issue

Five hundred eighty-four elderly persons underwent brain MRI followed by segmentation of white matter hyperintensities. The location of white matter hyperintensities was assessed by voxel-based lesion-symptom mapping and graph-based models were used to assess lesion volumes in white matter tracts. Vascular cognitive impairment was influenced by the location of lesions. ![Graphic][1]</img> See p. 1946; Editorial, p. 1940 Patients with autopsy-confirmed microinfarcts had a higher frequency of macroinfarcts on antemortem MRI, suggesting shared risk factors or pathophysiology. Patients with microinfarcts had higher brain atrophy rates independent … [1]: /embed/inline-graphic-1.gif

Strategic white matter tracts for processing speed deficits in age-related small vessel disease.

Cerebral small vessel disease is the most common cause of vascular cognitive impairment and typically manifests with slowed processing speed. We investigated the impact of lesion location on processing speed in age-related small vessel disease. A total of 584 community-dwelling elderly underwent brain MRI followed by segmentation of white matter hyperintensities. Processing speed was determined by the timed measure of the Trail Making Test part B. The impact of the location of white matter hyperintensities was assessed by voxel-based lesion-symptom mapping and graph-based statistical models on regional lesion volumes in major white matter tracts. Voxel-based lesion-symptom mapping identified multiple voxel clusters where the presence of white matter hyperintensities was associated with slower performance on the Trail Making Test part B. Clusters were located bilaterally in the forceps minor and anterior thalamic radiation. Region of interest-based Bayesian network analyses on lesion volumes within major white matter tracts depicted the same tracts as direct predictors for an impaired Trail Making Test part B performance. Our findings highlight damage to frontal interhemispheric and thalamic projection fiber tracts harboring frontal-subcortical neuronal circuits as a predictor for processing speed performance in age-related small vessel disease.

Application of variable threshold intensity to segmentation for white matter hyperintensities in fluid attenuated inversion recovery magnetic resonance images

White matter hyperintensities (WMHs) are regions of abnormally high intensity on T2-weighted or fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI). Accurate and reproducible automatic segmentation of WMHs is important since WMHs are often seen in the elderly and are associated with various geriatric and psychiatric disorders. We developed a fully automated monospectral segmentation method for WMHs using FLAIR MRIs. Through this method, we introduce an optimal threshold intensity (I O ) for segmenting WMHs, which varies with WMHs volume (V WMH), and we establish the I O -V WMH relationship. Our method showed accurate validations in volumetric and spatial agreements of automatically segmented WMHs compared with manually segmented WMHs for 32 confirmatory images. Bland-Altman values of volumetric agreement were 0.96 ± 8.311ml (bias and 95% confidence interval), and the similarity index of spatial agreement was 0.762 ± 0.127 (mean ± standard deviation). Furthermore, similar validation accuracies were obtained in the images acquired from different scanners. The proposed segmentation method uses only FLAIR MRIs, has the potential to be accurate with images obtained from different scanners, and can be implemented with a fully automated procedure. In our study, validation results were obtained with FLAIR MRIs from only two scanner types. The design of the method may allow its use in large multicenter studies with correct efficiency.

Efficient brain lesion segmentation using multi‐modality tissue‐based feature selection and support vector machines

Support vector machines (SVM) are machine learning techniques that have been used for segmentation and classification of medical images, including segmentation of white matter hyper-intensities (WMH). Current approaches using SVM for WMH segmentation extract features from the brain and classify these followed by complex post-processing steps to remove false positives. The method presented in this paper combines advanced pre-processing, tissue-based feature selection and SVM classification to obtain efficient and accurate WMH segmentation. Features from 125 patients, generated from up to four MR modalities [T1-w, T2-w, proton-density and fluid attenuated inversion recovery(FLAIR)], differing neighbourhood sizes and the use of multi-scale features were compared. We found that although using all four modalities gave the best overall classification (average Dice scores of 0.54 ± 0.12, 0.72 ± 0.06 and 0.82 ± 0.06 respectively for small, moderate and severe lesion loads); this was not significantly different (p = 0.50) from using just T1-w and FLAIR sequences (Dice scores of 0.52 ± 0.13, 0.71 ± 0.08 and 0.81 ± 0.07). Furthermore, there was a negligible difference between using 5 × 5 × 5 and 3 × 3 × 3 features (p = 0.93). Finally, we show that careful consideration of features and pre-processing techniques not only saves storage space and computation time but also leads to more efficient classification, which outperforms the one based on all features with post-processing.

White matter hyperintensities segmentation: a new semi-automated method

White matter hyperintensities (WMH) are brain areas of increased signal on T2-weighted or fluid-attenuated inverse recovery magnetic resonance imaging (MRI) scans. In this study we present a new semi-automated method to measure WMH load that is based on the segmentation of the intensity histogram of fluid-attenuated inversion recovery images. Thirty patients with mild cognitive impairment with variable WMH load were enrolled. The semi-automated WMH segmentation included removal of non-brain tissue, spatial normalization, removal of cerebellum and brain stem, spatial filtering, thresholding to segment probable WMH, manual editing for correction of false positives and negatives, generation of WMH map, and volumetric estimation of the WMH load. Accuracy was quantitatively evaluated by comparing semi-automated and manual WMH segmentations performed by two independent raters. Differences between the two procedures were assessed using Student’s t-tests and similarity was evaluated using linear regression model and Dice similarity coefficient (DSC). The volumes of the manual and semi-automated segmentations did not statistically differ (t-value = -1.79, DF = 29, p = 0.839 for rater 1; t-value = 1.113, DF = 29, p = 0.2749 for rater 2), were highly correlated [R2 = 0.921, F(1,29) = 155.54, p < 0.0001 for rater 1; R2 = 0.935, F(1,29) = 402.709, p < 0.0001 for rater 2] and showed a very strong spatial similarity (mean DSC = 0.78, for rater 1 and 0.77 for rater 2). In conclusion, our semi-automated method to measure the load of WMH is highly reliable and could represent a good tool that could be easily implemented in routinely neuroimaging analyses to map clinical consequences of WMH.

Contrast-Based Fully Automatic Segmentation of White Matter Hyperintensities: Method and Validation

White matter hyperintensities (WMH) on T2 or FLAIR sequences have been commonly observed on MR images of elderly people. They have been associated with various disorders and have been shown to be a strong risk factor for stroke and dementia. WMH studies usually required visual evaluation of WMH load or time-consuming manual delineation. This paper introduced WHASA (White matter Hyperintensities Automated Segmentation Algorithm), a new method for automatically segmenting WMH from FLAIR and T1 images in multicentre studies. Contrary to previous approaches that were based on intensities, this method relied on contrast: non linear diffusion filtering alternated with watershed segmentation to obtain piecewise constant images with increased contrast between WMH and surroundings tissues. WMH were then selected based on subject dependant automatically computed threshold and anatomical information. WHASA was evaluated on 67 patients from two studies, acquired on six different MRI scanners and displaying a wide range of lesion load. Accuracy of the segmentation was assessed through volume and spatial agreement measures with respect to manual segmentation; an intraclass correlation coefficient (ICC) of 0.96 and a mean similarity index (SI) of 0.72 were obtained. WHASA was compared to four other approaches: Freesurfer and a thresholding approach as unsupervised methods; k-nearest neighbours (kNN) and support vector machines (SVM) as supervised ones. For these latter, influence of the training set was also investigated. WHASA clearly outperformed both unsupervised methods, while performing at least as good as supervised approaches (ICC range: 0.87–0.91 for kNN; 0.89–0.94 for SVM. Mean SI: 0.63–0.71 for kNN, 0.67–0.72 for SVM), and did not need any training set.

Automatic segmentation of white matter hyperintensities by an extended FitzHugh &amp; nagumo reaction diffusion model

To evaluate the efficiency and reproducibility of the extended FitzHugh & Nagumo (FHN) reaction-diffusion model proposed in this study for white matter hyperintensities (WMH) segmentation. Five types of magnetic resonance T2-weighted fluid-attenuated inversion-recovery (T2FLAIR) images of 127 patients with different scanning parameters from five clinical scanner systems were selected for this study. After skull and scalp removal and denoise, the T2FLAIR images were processed by the proposed extended FHN model to obtain WMH. This new technique replaced the global threshold constant with a local threshold matrix. There was no significant difference between the segmentation results of the training set and the manual contouring against those between the test set and the manual contouring based on similarity index (SI) values (P = 0.5217). The SI values of the five types of T2FLAIR images were 86.0% ± 15.4%, 85.8% ± 10.5%, 84.1% ± 14.8%, 87.2% ± 14.6%, 86.3% ± 12.7%, respectively, comparing the segmentation results using the proposed method to the manual delineations. The overall SI value of the images was 86.5% ± 14.5%. This approach also demonstrated a better WMH segmentation performance over its classic form (P < 0.001). The proposed approach is efficient and could provide a more effective and convenient tool for clinical quantitative WMH analysis.

Fully automated pipeline for quantification and localization of white matter hyperintensity in brain magnetic resonance image

AbstractAutomated white matter hyperintensity (WMH) segmentation on magnetic resonance imaging is greatly advantageous for various clinical studies using large‐sample data. Accurate localization of WMH can provide more beneficial information for clinical studies, as differences of regional WMH existence may be linked to clinical symptoms. We suggest a fully automated method for WMH quantification and localization without human interaction using T1‐weighted and fluid‐attenuated inversion‐recovery (FLAIR) images. The known sources of false‐positive results in the subarachnoid space and brain‐cerebrospinal fluid (CSF) interface were removed by applying a WMH candidate‐region mask. WMH segmentation was performed based on the Markov random field model. The intensity‐substitution method was developed for the accurate localization of WMH, with proper tissue classification and nonlinear registration. The performance of the method was evaluated via comparison with manual delineation; the similarity index and the overlap ratio were 89.94 and 81.90, respectively. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 193–200, 2011

Correlations between MRI white matter lesion location and executive function and episodic memory

MRI white matter hyperintensity (WMH) volume is associated with cognitive impairment. We hypothesized that specific loci of WMH would correlate with cognition even after accounting for total WMH volume. Subjects were identified from a prospective community-based study: 40 had normal cognition, 94 had mild impairment (defined here as a Clinical Dementia Rating [CDR] score of 0.5 without dementia), and 11 had mild Alzheimer's dementia. Factor analysis of a 22-item neuropsychological battery yielded 4 factors (episodic memory, executive function, spatial skills, and general knowledge). MRI WMH segmentation and analysis was performed using FreeSurfer software. Higher WMH volume was independently associated with lower executive function and episodic memory factor scores. Voxel-based general linear models showed loci where WMH was strongly inversely associated with specific cognitive factor scores (p < 0.001), controlling for age, education, sex, APOE genotype, and total WMH volume. For episodic memory, clusters were observed in bilateral temporal-occipital and right parietal periventricular white matter, and the left anterior limb of the internal capsule. For executive function, clusters were observed in bilateral inferior frontal white matter, bilateral temporal-occipital and right parietal periventricular white matter, and the anterior limb of the internal capsule bilaterally. Specific WMH loci are closely associated with executive function and episodic memory, independent of total WMH volume. The anatomic locations suggest that WMH may cause cognitive impairment by affecting connections between cortex and subcortical structures, including the thalamus and striatum, or connections between the occipital lobe and frontal or parietal lobes.

Validation of Automated White Matter Hyperintensity Segmentation

Introduction. White matter hyperintensities (WMHs) are a common finding on MRI scans of older people and are associated with vascular disease. We compared 3 methods for automatically segmenting WMHs from MRI scans. Method. An operator manually segmented WMHs on MRI images from a 3T scanner. The scans were also segmented in a fully automated fashion by three different programmes. The voxel overlap between manual and automated segmentation was compared. Results. Between observer overlap ratio was 63%. Using our previously described in-house software, we had overlap of 62.2%. We investigated the use of a modified version of SPM segmentation; however, this was not successful, with only 14% overlap. Discussion. Using our previously reported software, we demonstrated good segmentation of WMHs in a fully automated fashion.

MRI Markers of Small Vessel Disease in Lobar and Deep Hemispheric Intracerebral Hemorrhage

MRI evidence of small vessel disease is common in intracerebral hemorrhage (ICH). We hypothesized that ICH caused by cerebral amyloid angiopathy (CAA) or hypertensive vasculopathy would have different distributions of MRI T2 white matter hyperintensity (WMH) and microbleeds. Data were analyzed from 133 consecutive patients with primary supratentorial ICH and adequate MRI sequences. CAA was diagnosed using the Boston criteria. WMH segmentation was performed using a validated semiautomated method. WMH and microbleeds were compared according to site of symptomatic hematoma origin (lobar versus deep) or by pattern of hemorrhages, including both hematomas and microbleeds, on MRI gradient recalled echo sequence (grouped as lobar only-probable CAA, lobar only-possible CAA, deep hemispheric only, or mixed lobar and deep hemorrhages). Patients with lobar and deep hemispheric hematoma had similar median normalized WMH volumes (19.5 cm versus 19.9 cm(3), P=0.74) and prevalence of >or=1 microbleed (54% versus 52%, P=0.99). The supratentorial WMH distribution was similar according to hemorrhage location category; however, the prevalence of brain stem T2 hyperintensity was lower in lobar hematoma versus deep hematoma (54% versus 70%, P=0.004). Mixed ICH was common (23%). Patients with mixed ICH had large normalized WMH volumes and a posterior distribution of cortical hemorrhages similar to that seen in CAA. WMH distribution is largely similar between CAA-related and non-CAA-related ICH. Mixed lobar and deep hemorrhages are seen on MRI gradient recalled echo sequence in up to one fourth of patients; in these patients, both hypertension and CAA may be contributing to the burden of WMH.

Automatic segmentation of white matter hyperintensities in the elderly using FLAIR images at 3T

PurposeTo determine the precision and accuracy of an automated method for segmenting white matter hyperintensities (WMH) on fast fluid-attenuated inversion-recovery (FLAIR) images in elderly brains at 3T.Materials and MethodsFLAIR images from 18 individuals (60–82 years, 9 females) with WMH burdens ranging from 1–80 cm3 were used. The protocol included the removal of clearly hyperintense voxels; two-class fuzzy C-means clustering (FCM); and thresholding to segment probable WMH. Two false-positive minimization (FPM) methods using white matter templates were tested. Precision was assessed by adding synthetic hyperintense voxels to brain slices. Accuracy was validated by comparing automatic and manual segmentations. Whole-brain, voxel-wise metrics of similarity, under- and overestimation were used to evaluate both precision and accuracy.ResultsPrecision was high, as the lowest accuracy in the synthetic datasets was 93%. Both FPM strategies successfully improved overall accuracy. Whole-brain accuracy for the FCM segmentation alone ranged from 45%–81%, which improved to 75%–85% using the FPM strategies.ConclusionThe method was accurate across the range of WMH burden typically seen in the elderly. Accuracy levels achieved or exceeded those of other approaches using multispectral and/or more sophisticated pattern recognition methods. J. Magn. Reson. Imaging 2010;31:1311–1322. © 2010 Wiley-Liss, Inc.

Development and validation of morphological segmentation of age-related cerebral white matter hyperintensities

Accurate automated segmentation of age-related white matter hyperintensity (WMH) is desirable for topological studies and those involving large samples. We assessed the accuracy of a novel automated method for segmentation of WMH on magnetic resonance imaging (MRI) in a randomly selected population-based sample of older people aged >60 years. The method combined morphological segmentation and statistical classifiers. Validation of this method was performed against expert manual segmentation in a sample of 30 scans, and against semi-automated segmentation in 202 scans. Its performance was also compared with those of other known methods derived from simple thresholding or Gaussian mixture modelling.Automated morphological segmentation combined with an adaptive boosting statistical classifier showed substantial agreement with manual segmentation, with an intraclass correlation coefficient (ICC) of 0.90 (95% confidence interval [CI], 0.80–0.95) for WMH volume and median similarity index (SI) of 0.58 (interquartile range [IQR] 0.50–0.65). The method also showed similarly high levels of agreement with semi-automated segmentation, with ICC 0.92 (95% CI 0.89–0.93) and median SI 0.56 (IQR 0.49–0.66). Its best performance was observed for the highest tertile of WMH volume. Threshold-based and Gaussian mixture model-driven automated segmentation generally did not perform well in this study.