Automatic segmentation of different-sized white matter lesions by voxel probability estimation

Anbeek, Petronella; Vincken, Koen L.; Osch, Matthias J.P. van; Bisschops, Robertus H.C.; Grond, Jeroen van der

doi:10.1016/j.media.2004.06.019

Cited by 108 publications

(82 citation statements)

References 31 publications

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“…9 To facilitate time-efficient, reproducible, and accurate lesion-load detection, many algorithms have been proposed for fully automated computer-assistive solutions. 3,18 These methods use different principles, including intensity-gradient features, 19 intensity thresholding, 20 intensity-histogram modeling of expected tissue classes, [21][22][23] fuzzy connectedness, 24 identification of nearest neighbors in a feature space, 25,26 or a combination of these. Methods such as Bayesian inference, expectation maximization, support-vector machines, k-nearest neighbor majority voting, and artificial neural networks are algorithmic approaches used to op- Comparing the number of study pairs improved with demyelinating lesions detected by both readers when using the newly developed assistive software to the issued radiology report.…”

Section: Discussionmentioning

confidence: 99%

Improving Multiple Sclerosis Plaque Detection Using a Semiautomated Assistive Approach

Heerden

Rawlinson

Zhang

et al. 2015

AJNR Am J Neuroradiol

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Section: Discussionmentioning

confidence: 99%

Improving Multiple Sclerosis Plaque Detection Using a Semiautomated Assistive Approach

Heerden

Rawlinson

Zhang

et al. 2015

AJNR Am J Neuroradiol

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“…However, semi-automated methods are still labor intensive and time consuming and require well-trained analysts. Fully automated quantification methods of WMLs are not yet optimal either since manual correction is needed to improve accuracy (72,73). Although some semi-automated methods include sub-segmentation of PVWML and DWML using distance from the ventricular surface (5,29), sub-segmentation has not yet been included in fully automated methods.…”

Section: Volumetric and Quantitative Scoring Of Wmlsmentioning

confidence: 99%

Classification of White Matter Lesions on Magnetic Resonance Imaging in Elderly Persons

Kim

MacFall

Payne

2008

Biological Psychiatry

432

415

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White matter lesions, commonly seen on magnetic resonance images of elderly people, are related to various geriatric disorders including cerebrovascular diseases, cardiovascular diseases, dementia, and psychiatric disorders. Currently, white matter lesions are divided into periventricular white matter lesions and deep white matter lesions. Although the meaning of these terms vary by study and this dichotomization itself is still in debate, a possible dissimilarity in pathogenic mechanisms between periventricular white matter lesions and deep white matter lesions are providing some clues for understanding pathophysiology of many geriatric syndromes associated with white matter lesions.We have reviewed the distinctions between periventricular white matter lesions and deep white matter lesions in terms of etiology, histopathology, functional correlates, and imaging methodologies. We suggest a new sub-classification of white matter lesions which may have better etiological and functional relevance than the current simple dichotomization. The new categories are juxtaventricular, periventricular, deep white, and juxtacortical. This new classification scheme may contribute to reducing the heterogeneity of white matter lesion findings in future research.

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“…The lower similarity values for the low lesion loads are to be expected, since errors in the segmentation have a greater impact on the similarity score when the lesion load is lower. This has also been reported in previous studies [74,73,95].…”

Section: Segmentation Of White Matter Hyperintensities In Flair Imagessupporting

confidence: 91%

“…We use the following metrics for comparison: Dice Similarity Coefficient (DSC), Overlap Fraction (OF) and Extra Fraction (EF) [73]:…”

Section: Evaluation Metricsmentioning

confidence: 99%

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Towards earlier detection of Alzheimer's disease using magnetic resonance images

Simões¹

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Alzheimer's disease (AD) is the most common type of dementia and a major cause of disability worldwide. Early detection of AD is essential to provide the patients with adequate and timely treatments and to help researchers monitor their effectiveness. Structural Magnetic Resonance Imaging (MRI) is a diagnostic tool that provides high-resolution images and a high brain tissue contrast.MRI-based biomarkers have been investigated in an attempt to describe and quantify structural differences between groups of normal elderly controls and subjects suffering from AD. Additionally, classification methods have been proposed that use these biomarkers as features to distinguish between those groups, thereby also providing diagnostic value.Two main approaches have been extensively explored in the past decades to perform early-stage AD classification based on structural MR images. The first uses the volume and/or the shape of specific brain structures, such as the hippocampi and the entorhinal cortex. As a consequence, these methods rely substantially on the quality of: 1) the assumptions of which brain regions are affected at an early stage of AD; 2) the segmentation of these brain structures, which suffers from large variability across studies. Another major line of research overcomes the first drawback by using voxelwise measures, such as the probability maps of the brain tissues. However, these methods require a voxelwise inter-subject correspondence, which is difficult to achieve, particularly considering the large anatomical variability of the brain across different subjects.Besides the above-mentioned disadvantages of these two approaches, they both focus on structural (volume, shape, density) changes only. It has recently been considered that also the MR image intensities and textures can provide complementary information that is overlooked by the structural-based features.In this thesis, we propose methods to help diagnose AD at an early stage of development. In particular, we build on the existing literature on classification approaches that use MR image textures for early detection of AD.vi Firstly, we focus our analysis on a type of lesions in the white matter (white matter hyperintensities) that have been shown to play a role in cognitive decline. We propose a method to automatically segment these lesions from a single MRI modality that can be suitable for large-scale clinical trials. We show that our method, despite using less information, performs similarly to current state-of-the-art multimodal approaches. Afterwards, we evaluate the performance of white matter lesion texture descriptors in the detection of Mild Cognitive Impairment (MCI, a transitional stage between normal ageing and dementia). Results show that the textures are more discriminative than the widely used lesion volumes and locations.Secondly, we evaluate three approaches that use texture descriptors without requiring prior brain structure segmentations. The first one takes the graylevel histograms computed in the whole brain and in sev...

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Automatic segmentation of different-sized white matter lesions by voxel probability estimation

Cited by 108 publications

References 31 publications

Improving Multiple Sclerosis Plaque Detection Using a Semiautomated Assistive Approach

Improving Multiple Sclerosis Plaque Detection Using a Semiautomated Assistive Approach

Classification of White Matter Lesions on Magnetic Resonance Imaging in Elderly Persons

Towards earlier detection of Alzheimer's disease using magnetic resonance images

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