Validation and Optimization of BIANCA for the Segmentation of Extensive White Matter Hyperintensities

Development and validation of a two-stage convolutional neural network algorithm for segmentation of MRI white matter hyperintensities for longitudinal studies in CADASIL

BACKGROUND

Segmentation of white matter hyperintensities (WMH) in CADASIL, one of the most severe cerebral small vessel disease of genetic origin, is challenging.

METHOD

We adapted and validated an automatic method based on a convolutional neural network (CNN) algorithm and using a large dataset of 2D and/or 3D FLAIR and T1-weighted images acquired in 132 patients, to measure the progression of WMH in this condition.

RESULTS

The volume of WMH measured using this method correlated strongly with reference data validated by experts. WMH segmentation was also clearly improved compared to the BIANCA segmentation method. Combining two successive learning models was found to be of particular interest, reducing the number of false-positive voxels and the extent of under-segmentation detected after a single-stage process. With the two-stage approach, WMH progression correlated with measures derived from the reference masks for lesions increasing with age, and with the variable WMH progression trajectories at individual level. We also confirmed the expected effect of the initial load of WMH and the influence of the type of MRI acquisition on measures of this progression.

CONCLUSION

Altogether, our findings suggest that WMH progression in CADASIL can be measured automatically with adequate confidence by a CNN segmentation algorithm.

Evolution of white matter hyperintensity segmentation methods and implementation over the past two decades; an incomplete shift towards deep learning

This systematic review examines the prevalence, underlying mechanisms, cohort characteristics, evaluation criteria, and cohort types in white matter hyperintensity (WMH) pipeline and implementation literature spanning the last two decades. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we categorized WMH segmentation tools based on their methodologies from January 1, 2000, to November 18, 2022. Inclusion criteria involved articles using openly available techniques with detailed descriptions, focusing on WMH as a primary outcome. Our analysis identified 1007 visual rating scales, 118 pipeline development articles, and 509 implementation articles. These studies predominantly explored aging, dementia, psychiatric disorders, and small vessel disease, with aging and dementia being the most prevalent cohorts. Deep learning emerged as the most frequently developed segmentation technique, indicative of a heightened scrutiny in new technique development over the past two decades. We illustrate observed patterns and discrepancies between published and implemented WMH techniques. Despite increasingly sophisticated quantitative segmentation options, visual rating scales persist, with the SPM technique being the most utilized among quantitative methods and potentially serving as a reference standard for newer techniques. Our findings highlight the need for future standards in WMH segmentation, and we provide recommendations based on these observations.

Early remodeling and loss of light-induced dilation of retinal small arteries in CADASIL

A major hurdle to therapeutic development in cerebral small vessel diseases is the lack of in-vivo method that can be used repeatedly for evaluating directly cerebral microvessels. We hypothesised that Adaptive Optics (AO), which allows resolution images up to 1-2 μm/pixel at retinal level, could provide a biomarker for monitoring vascular changes in CADASIL, a genetic form of such condition. In 98 patients and 35 healthy individuals, the wall to lumen ratio (WLR), outer and inner diameter, wall thickness and wall cross-sectional area were measured in a parapapillary and/or paramacular retinal artery. The ratio of vessel diameters before and after light flicker stimulations was also calculated to measure vasoreactivity (VR). Multivariate mixed-model analysis showed that WLR was increased and associated with a larger wall thickness and smaller internal diameter of retinal arteries in patients. The difference was maximal at the youngest age and gradually reduced with aging. Average VR in patients was less than half of that of controls since the youngest age. Any robust association was found with clinical or imaging manifestations of the disease. Thus, AO enables the detection of early functional or structural vascular alterations in CADASIL but with no obvious link to the clinical or imaging severity.

Under pressure: the interplay of hypertension and white matter hyperintensities with cognition in chronic stroke aphasia

While converging research suggests that increased white matter hyperintensity load is associated with poorer cognition, and the presence of hypertension is associated with increased white matter hyperintensity load, the relationship among hypertension, cognition and white matter hyperintensities is not well understood. We sought to determine the effect of white matter hyperintensity burden on the relationship between hypertension and cognition in individuals with post-stroke aphasia, with the hypothesis that white matter hyperintensity load moderates the relationship between history of hypertension and cognitive function. Health history, Fazekas scores for white matter hyperintensities and Wechsler Adult Intelligence Scale Matrix Reasoning subtest scores for 79 people with aphasia collected as part of the Predicting Outcomes of Language Rehabilitation study at the Center for the Study of Aphasia Recovery at the University of South Carolina and the Medical University of South Carolina were analysed retrospectively. We found that participants with a history of hypertension had increased deep white matter hyperintensity severity (P < 0.001), but not periventricular white matter hyperintensity severity (P = 0.116). Moderation analysis revealed that deep white matter hyperintensity load moderates the relationship between high blood pressure and Wechsler Adult Intelligence Scale scores when controlling for age, education, aphasia severity and lesion volume. The interaction is significant, showing that a history of high blood pressure and severe deep white matter hyperintensities together are associated with poorer Matrix Reasoning scores. The overall model explains 41.85% of the overall variation in Matrix Reasoning score in this group of participants. These findings underscore the importance of considering cardiovascular risk factors in aphasia treatment, specifically hypertension and its relationship to brain health in post-stroke cognitive function.

Artificial intelligence for volumetric measurement of cerebral white matter hyperintensities on thick-slice fluid-attenuated inversion recovery (FLAIR) magnetic resonance images from multiple centers

We aimed to develop a new artificial intelligence software that can automatically extract and measure the volume of white matter hyperintensities (WMHs) in head magnetic resonance imaging (MRI) using only thick-slice fluid-attenuated inversion recovery (FLAIR) sequences from multiple centers. We enrolled 1092 participants in Japan, comprising the thick-slice Private Dataset. Based on 207 randomly selected participants, neuroradiologists annotated WMHs using predefined guidelines. The annotated images of participants were divided into training (n = 138) and test (n = 69) datasets. The WMH segmentation model comprised a U-Net ensemble and was trained using the Private Dataset. Two other models were trained for validation using either both thin- and thick-slice MRI datasets or the thin-slice dataset alone. The voxel-wise Dice similarity coefficient (DSC) was used as the evaluation metric. The model trained using only thick-slice MRI showed a DSC of 0.820 for the test dataset, which is comparable to the accuracy of human readers. The model trained with the additional thin-slice dataset showed only a slightly improved DSC of 0.822. This automatic WMH segmentation model comprising a U-Net ensemble trained on a thick-slice FLAIR MRI dataset is a promising new method. Despite some limitations, this model may be applicable in clinical practice.

Automatic segmentation of white matter hyperintensities in T2-FLAIR with AQUA: A comparative validation study against conventional methods

White matter hyperintensities (WMHs) are lesions in the white matter of the brain that are associated with cognitive decline and an increased risk of dementia. The manual segmentation of WMHs is highly time-consuming and prone to intra- and inter-variability. Therefore, automatic segmentation approaches are gaining attention as a more efficient and objective means to detect and monitor WMHs. In this study, we propose AQUA, a deep learning model designed for fully automatic segmentation of WMHs from T2-FLAIR scans, which improves upon our previous study for small lesion detection and incorporating a multicenter approach. AQUA implements a two-dimensional U-Net architecture and uses patch-based training. Additionally, the network was modified to include Bottleneck Attention Module on each convolutional block of both the encoder and decoder to enhance performance for small-sized WMH. We evaluated the performance and robustness of AQUA by comparing it with five well-known supervised and unsupervised methods for automatic segmentation of WMHs (LGA, LPA, SLS, UBO, and BIANCA). To accomplish this, we tested these six methods on the MICCAI 2017 WMH Segmentation Challenge dataset, which contains MRI images from 170 elderly participants with WMHs of presumed vascular origin, and assessed their robustness across multiple sites and scanner types. The results showed that AQUA achieved superior performance in terms of spatial (Dice = 0.72) and volumetric (logAVD = 0.10) agreement with the manual segmentation compared to the other methods. While the recall and F1-score were moderate at 0.49 and 0.59, respectively, they improved to 0.75 and 0.82 when excluding small lesions (≤ 6 voxels). Remarkably, despite being trained on a different dataset with different ethnic backgrounds, lesion loads, and scanners, AQUA's results were comparable to the top 10 ranked methods of the MICCAI challenge. The findings suggest that AQUA is effective and practical for automatic segmentation of WMHs from T2-FLAIR scans, which could help identify individuals at risk of cognitive decline and dementia and allow for early intervention and management.

Optimizing automated white matter hyperintensity segmentation in individuals with stroke

White matter hyperintensities (WMHs) are a risk factor for stroke. Consequently, many individuals who suffer a stroke have comorbid WMHs. The impact of WMHs on stroke recovery is an active area of research. Automated WMH segmentation methods are often employed as they require minimal user input and reduce risk of rater bias; however, these automated methods have not been specifically validated for use in individuals with stroke. Here, we present methodological validation of automated WMH segmentation methods in individuals with stroke. We first optimized parameters for FSL's publicly available WMH segmentation software BIANCA in two independent (multi-site) datasets. Our optimized BIANCA protocol achieved good performance within each independent dataset, when the BIANCA model was trained and tested in the same dataset or trained on mixed-sample data. BIANCA segmentation failed when generalizing a trained model to a new testing dataset. We therefore contrasted BIANCA's performance with SAMSEG, an unsupervised WMH segmentation tool available through FreeSurfer. SAMSEG does not require prior WMH masks for model training and was more robust to handling multi-site data. However, SAMSEG performance was slightly lower than BIANCA when data from a single site were tested. This manuscript will serve as a guide for the development and utilization of WMH analysis pipelines for individuals with stroke.

Phenotypic variability in 446 CADASIL patients: Impact of NOTCH3 gene mutation location in addition to the effects of age, sex and vascular risk factors

The recent discovery that the prevalence of cysteine mutations in the NOTCH3 gene responsible for CADASIL was more than 100 times higher in the general population than that estimated in patients highlighted that the mutation location in EGFr-like-domains of the NOTCH3 receptor could have a major effect on the phenotype of the disease. The exact impact of such mutations locations on the multiple facets of the disease has not been fully evaluated. We aimed to describe the phenotypic spectrum of a large population of CADASIL patients and to investigate how this mutation location influenced various clinical and imaging features of the disease. Both a supervised and a non-supervised approach were used for analysis. The results confirmed that the mutation location is strongly related to clinical severity and showed that this effect is mainly driven by a different development of the most damaging ischemic tissue lesions at cerebral level. These effects were detected in addition to those of aging, male sex, hypertension and hypercholesterolemia. The exact mechanisms relating the location of mutations along the NOTCH3 receptor, the amount or properties of the resulting NOTCH3 products accumulating in the vessel wall, and their final consequences at cerebral level remain to be determined.

Elderly CADASIL patients with intact neurological status

BACKGROUND AND PURPOSE

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is one of the most devastating cerebral small vessel diseases. However, despite its progression with aging, some patients remain neurologically intact (Nint) even when they get older. Their main characteristics are poorly known. We aimed to delineate their clinical, imaging, and molecular features.

METHODS

Individuals aged over 65 years were selected from a cohort of 472 CADASIL patients. Subjects who had no focal deficit, cognitive impairment, or disability were considered Nint. Their demographic, genetic, clinical, and imaging features were compared to those with permanent neurological symptoms (Nps).

RESULTS

Among 129 patients, 23 (17.8%) individuals were considered Nint. The frequency of vascular risk factors and NOTCH3 cysteine mutations in epidermal growth factor-like repeat (EGFr) domains 7-34 did not differ between Nint and Nps patients but Nint patients had less stroke events and were more likely to have migraine with aura. The number of lacunes and microbleeds and degree of brain atrophy were lower in the Nint group, but the volume of white matter hyperintensities did not differ between the two groups.

CONCLUSIONS

Nearly one in five CADASIL patients can remain Nint after the age of 65 years. Their clinical and imaging profile differed from that of other age-matched CADASIL patients. The location of NOTCH3 mutation inside or outside EGFr domains 1-6 cannot fully explain this discrepancy. The factors involved in their relative preservation of brain tissue from severe damage despite aging remain to be determined.

Effect of NOTCH3 EGFr Group, Sex, and Cardiovascular Risk Factors on CADASIL Clinical and Neuroimaging Outcomes

A retrospective study has shown that EGFr (epidermal growth factor–like repeat) group in the NOTCH3 gene is an important cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) disease modifier of age at first stroke and white matter hyperintensity (WMH) volume. No study has yet assessed the effect of other known CADASIL modifiers, that is, cardiovascular risk factors and sex, in the context of NOTCH3 EGFr group. In this study, we determined the relative disease-modifying effects of NOTCH3 EGFr group, sex and cardiovascular risk factor on disease severity in the first genotype-driven, large prospective CADASIL cohort study, using a comprehensive battery of CADASIL clinical outcomes and neuroimaging markers.

Cerebral Autosomal Dominant Arteriopathy With Subcortical Infarcts and Leukoencephalopathy Family Members With a Pathogenic NOTCH3 Variant Can Have a Normal Brain Magnetic Resonance Imaging and Skin Biopsy Beyond Age 50 Years

BACKGROUND

To determine whether extremely mild small vessel disease (SVD) phenotypes can occur in NOTCH3 variant carriers from Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL) pedigrees using clinical, genetic, neuroimaging, and skin biopsy findings.

METHODS

Individuals from CADASIL pedigrees fulfilling criteria for extremely mild NOTCH3-associated SVD (mSVDNOTCH3) were selected from the cross-sectional Dutch CADASIL cohort (n=200), enrolled between 2017 and 2020. Brain magnetic resonance imaging were quantitatively assessed for SVD imaging markers. Immunohistochemistry and electron microscopy was used to quantitatively assess and compare NOTCH3 ectodomain (NOTCH3ECD) aggregation and granular osmiophilic material deposits in the skin vasculature of mSVDNOTCH3 cases and symptomatic CADASIL patients.

RESULTS

Seven cases were identified that fulfilled the mSVDNOTCH3 criteria, with a mean age of 56.6 years (range, 50-72). All of these individuals harbored a NOTCH3 variant located in one of EGFr domains 7-34 and had a normal brain magnetic resonance imaging, except the oldest individual, aged 72, who had beginning confluence of WMH (Fazekas score 2) and 1 cerebral microbleed. mSVDNOTCH3 cases had very low levels of NOTCH3ECD aggregation in skin vasculature, which was significantly less than in symptomatic EGFr 7-34 CADASIL patients (P=0.01). Six mSVDNOTCH3 cases had absence of granular osmiophilic material deposits.

CONCLUSIONS

Our findings demonstrate that extremely mild SVD phenotypes can occur in individuals from CADASIL pedigrees harboring NOTCH3 EGFr 7-34 variants with normal brain magnetic resonance imaging up to age 58 years. Our study has important implications for CADASIL diagnosis, disease prediction, and the counseling of individuals from EGFr 7-34 CADASIL pedigrees.

The Epidermal Growth Factor Domain of the Mutation Does Not Appear to Influence Disease Progression in CADASIL When Brain Volume and Sex Are Taken into Account

BACKGROUND AND PURPOSE

By studying the evolution of brain volume across the life span in male and female patients, we aimed to understand how sex, brain volume, and the epidermal growth factor repeat domain of the mutation, the 3 major determinants of disability in CADASIL, interact in driving disease evolution.

MATERIALS AND METHODS

We used validated methods to model the evolution of normalized brain volume with age in male and female patients using nonparametric regression in a large, monocentric cohort with prospectively collected clinical and high-resolution MR imaging data. We used k-means clustering to test for the presence of different clinical course profiles.

RESULTS

We included 229 patients (mean age, 53 [SD, 12] years; 130 women). Brain volume was larger in women (mean size, 1024 [SD, 62] cm³ versus 979 [SD, 50] cm³; P < .001) and decreased regularly. In men, the relationship between brain volume and age unexpectedly suggested an increase in brain volume around midlife. Cluster analyses showed that this finding was related to the presence of a group of older male patients with milder symptoms and larger brain volumes, similar to findings of age-matched women. This group did not show specific epidermal growth factor repeat domain distribution.

CONCLUSIONS

Our results demonstrate a detrimental effect of male sex on brain volume throughout life in CADASIL. We identified a subgroup of male patients whose brain volume and clinical outcomes were similar to those of age-matched women. They did not have a specific distribution of the epidermal growth factor repeat domain, suggesting that yet-unidentified predictors may interact with sex and brain volume in driving disease evolution.

Performance of three freely available methods for extracting white matter hyperintensities: FreeSurfer, UBO Detector, and BIANCA

White matter hyperintensities (WMH) of presumed vascular origin are frequently found in MRIs of healthy older adults. WMH are also associated with aging and cognitive decline. Here, we compared and validated three algorithms for WMH extraction: FreeSurfer (T1w), UBO Detector (T1w + FLAIR), and FSL's Brain Intensity AbNormality Classification Algorithm (BIANCA; T1w + FLAIR) using a longitudinal dataset comprising MRI data of cognitively healthy older adults (baseline N = 231, age range 64-87 years). As reference we manually segmented WMH in T1w, three-dimensional (3D) FLAIR, and two-dimensional (2D) FLAIR images which were used to assess the segmentation accuracy of the different automated algorithms. Further, we assessed the relationships of WMH volumes provided by the algorithms with Fazekas scores and age. FreeSurfer underestimated the WMH volumes and scored worst in Dice Similarity Coefficient (DSC = 0.434) but its WMH volumes strongly correlated with the Fazekas scores (rs  = 0.73). BIANCA accomplished the highest DSC (0.602) in 3D FLAIR images. However, the relations with the Fazekas scores were only moderate, especially in the 2D FLAIR images (rs  = 0.41), and many outlier WMH volumes were detected when exploring within-person trajectories (2D FLAIR: ~30%). UBO Detector performed similarly to BIANCA in DSC with both modalities and reached the best DSC in 2D FLAIR (0.531) without requiring a tailored training dataset. In addition, it achieved very high associations with the Fazekas scores (2D FLAIR: rs  = 0.80). In summary, our results emphasize the importance of carefully contemplating the choice of the WMH segmentation algorithm and MR-modality.

Association of Blood Pressure, Its Treatment, and Treatment Efficacy With Volume of White Matter Hyperintensities in the Population-Based 1000BRAINS Study

[Figure: see text].

NOTCH3 variant position is associated with NOTCH3 aggregation load in CADASIL vasculature

Aims

CADASIL, the most prevalent hereditary cerebral small vessel disease, is caused by cysteine‐altering NOTCH3 variants (NOTCH3^cys) leading to vascular NOTCH3 protein aggregation. It has recently been shown that variants located in one of NOTCH3 protein epidermal growth‐factor like repeat (EGFr) domains 1–6, are associated with a more severe phenotype than variants located in one of the EGFr domains 7–34. The underlying mechanism for this genotype–phenotype correlation is unknown. The aim of this study was to analyse whether NOTCH3^cys variant position is associated with NOTCH3 protein aggregation load.

Methods

We quantified vascular NOTCH3 aggregation in skin biopsies (n = 25) and brain tissue (n = 7) of CADASIL patients with a NOTCH3^cys EGFr 1–6 variant or a EGFr 7–34 variant, using NOTCH3 immunohistochemistry (NOTCH3 score) and ultrastructural analysis of granular osmiophilic material (GOM count). Disease severity was assessed by neuroimaging (lacune count and white matter hyperintensity volume) and disability (modified Rankin scale).

Results

Patients with NOTCH3^cys EGFr 7–34 variants had lower NOTCH3 scores (P = 1.3·10⁻⁵) and lower GOM counts (P = 8.2·10⁻⁵) than patients with NOTCH3^cys EGFr 1–6 variants in skin vessels. A similar trend was observed in brain vasculature. In the EGFr 7–34 group, NOTCH3 aggregation levels were associated with lacune count (P = 0.03) and white matter hyperintensity volume (P = 0.02), but not with disability.

Conclusions

CADASIL patients with an EGFr 7–34 variant have significantly less vascular NOTCH3 aggregation than patients with an EGFr 1–6 variant. This may be one of the factors underlying the difference in disease severity between NOTCH3^cys EGFr 7–34 and EGFr 1–6 variants.

Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review

OBJECTIVES

Medical image analysis practices face challenges that can potentially be addressed with algorithm-based segmentation tools. In this study, we map the field of automatic MR brain lesion segmentation to understand the clinical applicability of prevalent methods and study designs, as well as challenges and limitations in the field.

DESIGN

Scoping review.

SETTING

Three databases (PubMed, IEEE Xplore and Scopus) were searched with tailored queries. Studies were included based on predefined criteria. Emerging themes during consecutive title, abstract, methods and whole-text screening were identified. The full-text analysis focused on materials, preprocessing, performance evaluation and comparison.

RESULTS

Out of 2990 unique articles identified through the search, 441 articles met the eligibility criteria, with an estimated growth rate of 10% per year. We present a general overview and trends in the field with regard to publication sources, segmentation principles used and types of lesions. Algorithms are predominantly evaluated by measuring the agreement of segmentation results with a trusted reference. Few articles describe measures of clinical validity.

CONCLUSIONS

The observed reporting practices leave room for improvement with a view to studying replication, method comparison and clinical applicability. To promote this improvement, we propose a list of recommendations for future studies in the field.

Automatic segmentation of white matter hyperintensities from brain magnetic resonance images in the era of deep learning and big data - A systematic review

BACKGROUND

White matter hyperintensities (WMH), of presumed vascular origin, are visible and quantifiable neuroradiological markers of brain parenchymal change. These changes may range from damage secondary to inflammation and other neurological conditions, through to healthy ageing. Fully automatic WMH quantification methods are promising, but still, traditional semi-automatic methods seem to be preferred in clinical research. We systematically reviewed the literature for fully automatic methods developed in the last five years, to assess what are considered state-of-the-art techniques, as well as trends in the analysis of WMH of presumed vascular origin.

METHOD

We registered the systematic review protocol with the International Prospective Register of Systematic Reviews (PROSPERO), registration number - CRD42019132200. We conducted the search for fully automatic methods developed from 2015 to July 2020 on Medline, Science direct, IEE Explore, and Web of Science. We assessed risk of bias and applicability of the studies using QUADAS 2.

RESULTS

The search yielded 2327 papers after removing 104 duplicates. After screening titles, abstracts and full text, 37 were selected for detailed analysis. Of these, 16 proposed a supervised segmentation method, 10 proposed an unsupervised segmentation method, and 11 proposed a deep learning segmentation method. Average DSC values ranged from 0.538 to 0.91, being the highest value obtained from an unsupervised segmentation method. Only four studies validated their method in longitudinal samples, and eight performed an additional validation using clinical parameters. Only 8/37 studies made available their methods in public repositories.

CONCLUSIONS

We found no evidence that favours deep learning methods over the more established k-NN, linear regression and unsupervised methods in this task. Data and code availability, bias in study design and ground truth generation influence the wider validation and applicability of these methods in clinical research.

Automated lesion segmentation with BIANCA: Impact of population-level features, classification algorithm and locally adaptive thresholding

White matter hyperintensities (WMH) or white matter lesions exhibit high variability in their characteristics both at population- and subject-level, making their detection a challenging task. Population-level factors such as age, vascular risk factors and neurodegenerative diseases affect lesion load and spatial distribution. At the individual level, WMH vary in contrast, amount and distribution in different white matter regions. In this work, we aimed to improve BIANCA, the FSL tool for WMH segmentation, in order to better deal with these sources of variability. We worked on two stages of BIANCA by improving the lesion probability map estimation (classification stage) and making the lesion probability map thresholding stage automated and adaptive to local lesion probabilities. Firstly, in order to take into account the effect of population-level factors, we included population-level lesion probabilities, modelled with respect to a parametric factor (e.g. age), in the classification stage. Secondly, we tested BIANCA performance when using four alternative classifiers commonly used in the literature with respect to K-nearest neighbour algorithm (currently used for lesion probability map estimation in BIANCA). Finally, we propose LOCally Adaptive Threshold Estimation (LOCATE), a supervised method for determining optimal local thresholds to apply to the estimated lesion probability map, as an alternative option to global thresholding (i.e. applying the same threshold to the entire lesion probability map). For these experiments we used data from a neurodegenerative cohort, a vascular cohort and the cohorts available publicly as a part of a segmentation challenge. We observed that including population-level parametric lesion probabilities with respect to age and using alternative machine learning techniques provided negligible improvement. However, LOCATE provided a substantial improvement in the lesion segmentation performance, when compared to the global thresholding. It allowed to detect more deep lesions and provided better segmentation of periventricular lesion boundaries, despite the differences in the lesion spatial distribution and load across datasets. We further validated LOCATE on a cohort of CADASIL (Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy) patients, a genetic form of cerebral small vessel disease, and healthy controls, showing that LOCATE adapts well to wide variations in lesion load and spatial distribution.

Performance of five automated white matter hyperintensity segmentation methods in a multicenter dataset

White matter hyperintensities (WMHs) are a common manifestation of cerebral small vessel disease, that is increasingly studied with large, pooled multicenter datasets. This data pooling increases statistical power, but poses challenges for automated WMH segmentation. Although there is extensive literature on the evaluation of automated WMH segmentation methods, such evaluations in a multicenter setting are lacking. We performed WMH segmentations in sixty patients scanned on six different magnetic resonance imaging (MRI) scanners (10 patients per scanner) using five freely available and fully-automated WMH segmentation methods (Cascade, kNN-TTP, Lesion-TOADS, LST-LGA and LST-LPA). Different MRI scanner vendors and field strengths were included. We compared these automated WMH segmentations with manual WMH segmentations as a reference. Performance of each method both within and across scanners was assessed using spatial and volumetric correspondence with the reference segmentations by Dice's similarity coefficient (DSC) and intra-class correlation coefficient (ICC) respectively. We found the best performance, both within and across scanners, for kNN-TTP, followed by LST-LPA and LST-LGA, with worse performance for Lesion-TOADS and Cascade. Our findings can serve as a guide for choosing a method and also highlight the importance to further improve and evaluate consistency of methods in a multicenter setting.