Automatic segmentation and volumetric quantification of white matter hyperintensities on fluid-attenuated inversion recovery images using the extreme value distribution

Wang, Rui; Li, Chao; Wang, Jie; Wei, Xiaoer; Li, Yuehua; Zhu, Yuemin; Zhang, Su

doi:10.1007/s00234-014-1466-4

Cited by 16 publications

(14 citation statements)

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“…Intensity inhomogeneity correction was reported in 17/37 studies, and it was always performed using a well-known tool: N3 (or N4), SPM, FSL-FAST or the Nu estimate. N3 and its newer version N4, were the tools most commonly used for intensity inhomogeneity correction (Stone et al, 2016;Bowles, et al, 2017;Dadar et al, 2017a;Van Opbroek, Ikram, Vernooij & de Bruijne, 2015a, 2015bDamangir et al, 2017;Roy et al, 2015;Wang et al, 2015;Zhan et al, 2015;Zhan et al, 2017;Atlason et al, 2019;Ding et al, 2020). Non-local means was the only filtering technique used by the two studies that reported having included noise removal within their preprocessing steps (Manjón et al, 2018;Dadar et al, 2017a).…”

Section: Pre-processing Methodsmentioning

confidence: 99%

“…Computing and Computer Assisted Intervention (MICCAI) segmentation challenges (Roy et al, 2015;Sudre et al, 2015;Van Obproek et al, 2015a, 2015bWang et al, 2015;Valverde et al, 2017;Zhan et al, 2017;Knight et al, 2018;Li et al, 2018;Manjón et al, 2018;Moeskops et al, 2018;Sundaresan et al, 2019;Wu, Zhang, Wang & Tang, 2019;Liu et al, 2020). Nine of them also used additional datasets or patient data from clinics.…”

Section: Sample Characteristicsmentioning

confidence: 99%

“…Nine of them also used additional datasets or patient data from clinics. Out of 37, twelve studies reported using data from prospective studies or clinics (Atlason et al, 2019;Bowles et al, 2017;Guerrero et al, 2017;Hong et al, 2020;Moeskops et al, 2018;Ling et al, 2018;Park et al, 2018;Qin et al, 2018;Rincón et al, 2017;Roy et al, 2015;Sundaresan et al, 2019;Wang et al, 2015). Four studies used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (Sudre et al, 2017;Dadar et al, 2017a;Rachmadi et al, 2018Rachmadi et al, , 2020, of which only two declared the subset used (Rachmadi et al, 2018(Rachmadi et al, , 2020.…”

Section: Sample Characteristicsmentioning

confidence: 99%

“…Twelve studies used data only acquired at 1.5T, and twelve used data only acquired at 3T. Only 11/37 studies used data acquired at both 1.5T and 3T (Atlason et al, 2019;Dadar et al, 2017a;Jiang et al, 2018;Knight et al, 2018;Li et al, 2018;Ling et al, 2018;Liu et al, 2020;Manjón et al, 2018;Sundaresan et al, 2019;Wang et al, 2015;Wu, Zhang, Wang & Tang, 2019).…”

Section: Risk Of Bias Assessment Within Studiesmentioning

confidence: 99%

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Automatic Segmentation of White Matter Hyperintensities from Brain Magnetic Resonance Images in the Era of Deep Learning and Big Data – A Systematic Review

Balakrishnan

Hernández

Farrall

2020

Preprint

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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 0.93, being the highest value obtained from a deep learning 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 method in public repositories. Conclusions: Although deep learning methods reported highly accurate results, we found no evidence that favours them 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.

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Section: Pre-processing Methodsmentioning

confidence: 99%

Section: Sample Characteristicsmentioning

confidence: 99%

Section: Sample Characteristicsmentioning

confidence: 99%

Section: Risk Of Bias Assessment Within Studiesmentioning

confidence: 99%

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Automatic Segmentation of White Matter Hyperintensities from Brain Magnetic Resonance Images in the Era of Deep Learning and Big Data – A Systematic Review

Balakrishnan

Hernández

Farrall

2020

Preprint

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“…of the proposed methods use registration, brain extraction, bias field correction, and segmentation tools available in freely available toolkits; these include the SPM 8 toolkit[96,101,104,111,114,119,118,125,135] and the FSL 9 toolkit[64,99,106,116,117,124,125,133,137], as well as bias correction…”

mentioning

confidence: 99%

Voxel-Wise Image Analysis for White Matter Hyperintensity Segmentation

Knight¹

2018

Preprint

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White matter hyperintensities (WMH) are regions of increased pixel intensity in T2-weighted MRI which are correlated with several neurodegenerative diseases. Human segmentation of WMH is time consuming and inconsistent, motivating automation of WMH segmentation. While many algorithms for this task have previously been proposed, few have been validated on MRI from different sources, despite the sensitivity of most algorithms to source-specific image features. This thesis presents a segmentation algorithm called “Voxel-Wise Logistic Regression” (VLR), which provides both good interpretability and segmentation performance. VLR uses FLAIR MRI to estimate the WMH class probability image using spatially varying logistic parameters β(x). These “parameter images” also concisely summarize the model class discrimination. Additionally, a validation framework called “Leave-One-Source-Out Cross Validation” (LOSO-CV) is introduced, which provides more realistic estimation of model performance on “never-before-seen” MRI sources. Segmentation performance of the VLR model under LOSO-CV is presented using 96 open-source images from 7 MRI sources.

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

Rahmani,

Dierker,

Yaeger

et al. 2024

Brain Imaging and Behavior

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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.

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Automatic segmentation and volumetric quantification of white matter hyperintensities on fluid-attenuated inversion recovery images using the extreme value distribution

Abstract: The proposed EVD-based segmentation framework is a promising, effective, and convenient tool for volumetric quantification and further study of WMHs in aging and dementia.

Cited by 16 publications

References 50 publications

Automatic Segmentation of White Matter Hyperintensities from Brain Magnetic Resonance Images in the Era of Deep Learning and Big Data – A Systematic Review

Automatic Segmentation of White Matter Hyperintensities from Brain Magnetic Resonance Images in the Era of Deep Learning and Big Data – A Systematic Review

Voxel-Wise Image Analysis for White Matter Hyperintensity Segmentation

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

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