An improved algorithm of white matter hyperintensity detection in elderly adults

Ding, Tonghai; Cohen, Ann D.; O’Connor, EE; Karim, Helmet T.; Crainiceanu, Adina; Muschelli, John; López, Oscar L.; Klunk, William E.; Aizenstein, HJ; Krafty, Robert T.; Crainiceanu, CM; Tudorascu, DL

doi:10.1016/j.nicl.2019.102151

Cited by 19 publications

(19 citation statements)

References 27 publications

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“…FLIRT (Qin et al, 2018;Griffanti et al, 2016;Guerrero et al, 2017;Sundaresan et al, 2019;Damangir et al, 2017;Rachmadi et al, 2018Rachmadi et al, , 2020Ghafoorian et al, 2017;Hong et al, 2020;Ding et al, 2020), three used ANTs (Schirmer et al, 2019;Manjón et al, 2018;Stone et al, 2016), five used SPM Roy et al, 2015;Jiang et al, 2018;Knight et al, 2018;Valverde et al, 2017), one study used elastix (Moskops et al, 2018), one study used 3DSlicer…”

Section: Pre-processing Methodsmentioning

confidence: 99%

“…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%

“…From the 10 studies that proposed an unsupervised segmentation method (i.e., 27% of the total number of studies included), one used deep learning (Atlason et al, 2019). In total, eleven studies used Convolutional Neural Networks (Rachmadi, et al, 2018;Li et al, 2018;Guerrero et al, 2017;Moeskops et al, 2018;Ghafoorian et al, 2016;Hong et al, 2020;Manjón et al, 2018;Liu et al, 2020;Diniz et al, 2018;Schirmer et al, 2019), four studies proposed a method based on k-nearest neighbours (k-NN) (Sundaresan et al, 2019;Jiang et al, 2018;Ling et al, 2018;Griffanti et al, 2016), four studies proposed regression models (Knight et al, 2018;Dadar et al, 2017a;Zhan et al, 2017;Ding et al, 2020), and three studies used Random forest (RF) in their proposed algorithms (Stone et al, 2016;Park et al, 2018;Roy et al, 2015). Two studies proposed a method based on Fuzzy C mean algorithm (Zhan et al, 2015;Valverde et al, 2017) and three proposed improvements to a Gaussian Mixture Model framework (Sudre et al, 2015(Sudre et al, , 2017Fiford et al, 2020), both unsupervised methods.…”

Section: Segmentation Methodsmentioning

confidence: 99%

“…Samples involving more than 500 individuals were analysed in only two other studies (Griffanti et al, 2016 (n=559); Jiang et al, 2018 (n=566)). Eight studies used small relevant samples (i.e., fewer than 30 individuals with WMH of presumed vascular origin) (Ding et al, 2020;Rachmadi et al, 2020;Rincón et al ,2017;Roy et al, 2015;Stone et al, 2016, Van Opbroek et al, 2015a, 2015bValverde et al, 2017). Three of them used, in addition, data from MS patients (Rachmadi et al, 2020;Van Opbroek et al, 2015a, 2015b.…”

Section: Sample Characteristicsmentioning

confidence: 99%

“…Data inclusion and exclusion criteria were not explained in 22/37 studies. Of the studies that reported demographic information, five recruited healthy controls (Griffanti et al, 2016;Sundaresan et al, 2019;Damangir et al, 2017;Rincón et al, 2017;Ding et al, 2020). One study stated that the data selection and manipulation were blinded to clinical information (i.e., avoided clinical review bias) (Dadar et al, 2017a).…”

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: Pre-processing Methodsmentioning

confidence: 99%

Section: Segmentation Methodsmentioning

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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Machine Learning for Cerebrovascular Disorders

Chen

2023

Neuromethods

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Cerebrovascular disease refers to a group of conditions that affect blood flow and the blood vessels in the brain. It is one of the leading causes of mortality and disability worldwide, imposing a significant socioeconomic burden to society. Research on cerebrovascular diseases has been rapidly progressing leading to improvement in the diagnosis and management of patients nowadays. Machine learning holds many promises for further improving clinical care of these disorders. In this chapter, we will briefly introduce general information regarding cerebrovascular disorders and summarize some of the most promising fields in which machine learning shall be valuable to improve research and patient care. More specifically, we will cover the following cerebrovascular disorders: stroke (both ischemic and hemorrhagic), cerebral microbleeds, cerebral vascular malformations, intracranial aneurysms, and cerebral small vessel disease (white matter hyperintensities, lacunes, perivascular spaces).

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Stochastic Uncertainty Quantification Techniques Fail to Account for Inter-analyst Variability in White Matter Hyperintensity Segmentation

Philps,

del C. Valdes Hernandez,

Munoz Maniega

et al. 2024

Lecture Notes in Computer Science

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An improved algorithm of white matter hyperintensity detection in elderly adults

Cited by 19 publications

References 27 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

Machine Learning for Cerebrovascular Disorders

Stochastic Uncertainty Quantification Techniques Fail to Account for Inter-analyst Variability in White Matter Hyperintensity Segmentation

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