Literature DB >> 24493377

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

Byung Il Yoo1, Jung Jae Lee, Ji Won Han, San Yeo Wool Oh, Eun Young Lee, James R MacFall, Martha E Payne, Tae Hui Kim, Jae Hyoung Kim, Ki Woong Kim.   

Abstract

INTRODUCTION: 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.
METHODS: 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.
RESULTS: 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 ± 8.311 ml (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.
CONCLUSIONS: 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.

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Year:  2014        PMID: 24493377     DOI: 10.1007/s00234-014-1322-6

Source DB:  PubMed          Journal:  Neuroradiology        ISSN: 0028-3940            Impact factor:   2.804


  47 in total

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2.  Prevalence of white matter hyperintensities in a young healthy population.

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3.  Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study.

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4.  Reduction of dorsolateral prefrontal cortex gray matter in late-life depression.

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Journal:  Psychiatry Res       Date:  2011-05-18       Impact factor: 3.222

5.  The effect of white matter hyperintensity volume on brain structure, cognitive performance, and cerebral metabolism of glucose in 51 healthy adults.

Authors:  C DeCarli; D G Murphy; M Tranh; C L Grady; J V Haxby; J A Gillette; J A Salerno; A Gonzales-Aviles; B Horwitz; S I Rapoport
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8.  MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging.

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9.  FLAIR histogram segmentation for measurement of leukoaraiosis volume.

Authors:  C R Jack; P C O'Brien; D W Rettman; M M Shiung; Y Xu; R Muthupillai; A Manduca; R Avula; B J Erickson
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Review 10.  Role of magnetic resonance imaging in the diagnosis and monitoring of multiple sclerosis: consensus report of the White Matter Study Group.

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1.  Impact of White Matter Lesions on Depression in the Patients with Alzheimer's Disease.

Authors:  Jung Jae Lee; Eun Young Lee; Seok Bum Lee; Joon Hyuk Park; Tae Hui Kim; Hyun-Ghang Jeong; Jae Hyoung Kim; Ji Won Han; Ki Woong Kim
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2.  A hybrid approach based on logistic classification and iterative contrast enhancement algorithm for hyperintense multiple sclerosis lesion segmentation.

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Review 3.  Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review.

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

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5.  Multi-atlas based detection and localization (MADL) for location-dependent quantification of white matter hyperintensities.

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6.  Association of Low Blood Pressure with White Matter Hyperintensities in Elderly Individuals with Controlled Hypertension.

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7.  An improved algorithm of white matter hyperintensity detection in elderly adults.

Authors:  T Ding; A D Cohen; E E O'Connor; H T Karim; A Crainiceanu; J Muschelli; O Lopez; W E Klunk; H J Aizenstein; R Krafty; C M Crainiceanu; D L Tudorascu
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Review 8.  Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review.

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9.  Automated White Matter Hyperintensity Segmentation Using Bayesian Model Selection: Assessment and Correlations with Cognitive Change.

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Journal:  Neuroinformatics       Date:  2020-06

10.  Association between lifetime coffee consumption and late life cerebral white matter hyperintensities in cognitively normal elderly individuals.

Authors:  Jeongbin Park; Ji Won Han; Ju Ri Lee; Seonjeong Byun; Seung Wan Suh; Jae Hyoung Kim; Ki Woong Kim
Journal:  Sci Rep       Date:  2020-01-16       Impact factor: 4.379

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