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Journal ArticleDOI

Automatic segmentation of cerebral white matter hyperintensities using only 3D FLAIR images.

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TLDR
This work proposes an automatic lesion segmentation method that uses only three-dimensional fluid-attenuation inversion recovery (FLAIR) images and uses a modified context-sensitive Gaussian mixture model to determine voxel class probabilities, followed by correction of FLAIR artifacts.
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This article is published in Magnetic Resonance Imaging.The article was published on 2013-09-01. It has received 52 citations till now. The article focuses on the topics: Segmentation & Cognitive decline.

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Citations
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Journal ArticleDOI

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

TL;DR: The 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.
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Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities.

TL;DR: This paper applies and compares the proposed architectures for segmentation of white matter hyperintensities in brain MR images on a large dataset and observes that the CNNs that incorporate location information substantially outperform a conventional segmentation method with handcrafted features as well asCNNs that do not integrate location information.
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Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images.

TL;DR: The effectiveness and generalization capability of the proposed system show its potential for real‐world clinical practice and are the highest achieved in the challenge, suggesting the proposed method is the state‐of‐the‐art.
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A survey on U-shaped networks in medical image segmentations

TL;DR: A comprehensive literature review of U-shaped networks applied to medical image segmentation tasks, focusing on the architectures, extended mechanisms and application areas in these studies.
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Bayesian Model Selection for Pathological Neuroimaging Data Applied to White Matter Lesion Segmentation

TL;DR: This work proposes a hierarchical fully unsupervised model selection framework for neuroimaging data which enables the distinction between different types of abnormal image patterns without pathological a priori knowledge and demonstrates the ability to detect abnormal intensity clusters.
References
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Journal ArticleDOI

Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure

TL;DR: Two variants of fuzzy c-means clustering with spatial constraints, using the kernel methods, are proposed, inducing a class of robust non-Euclidean distance measures for the original data space to derive new objective functions and thus clustering theNon-E Euclidean structures in data.
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Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study

TL;DR: The prevalence and the degree of cerebralwhite matter lesions increased with age, and women tended to have a higher degree of white matter lesions than men, which may underlie the finding of a higher incidence of dementia in women than in men, particularly at later age.
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On convergence properties of the em algorithm for gaussian mixtures

TL;DR: The mathematical connection between the Expectation-Maximization (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite gaussian mixtures is built up and an explicit expression for the matrix is provided.
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The Significance of Cerebral White Matter Abnormalities 100 Years After Binswanger’s Report A Review

TL;DR: The objectives of this study were to determine whether the term Binswanger's disease merits acceptance as a distinct clinicopathologic entity, to deduce the clinical significance of these white matter abnormalities from the analysis of appropriate publications, and to evaluate studies that correlate in vivo changes in the cerebral white matter with pathological features.
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Automated segmentation of multiple sclerosis lesions by model outlier detection

TL;DR: A fully automated algorithm for segmentation of multiple sclerosis lesions from multispectral magnetic resonance (MR) images that performs intensity-based tissue classification using a stochastic model and simultaneously detects MS lesions as outliers that are not well explained by the model.
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