Multispectral MRI segmentation of age related white matter changes using a cascade of support vector machines

Damangir, Soheil; Manzouri, Amirhossein; Oppedal, Ketil; Carlsson, Stefan; Firbank, Michael; Sønnesyn, Hogne; Tysnes, Ole‐Bjørn; O’Brien, John T.; Beyer, Mona K.; Westman, Eric; Aarsland, Dag; Wahlund, Lars‐Olof; Spulber, Gabriela

doi:10.1016/j.jns.2012.07.064

Cited by 42 publications

(44 citation statements)

References 26 publications

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“…The reliability of this manual editing between two raters, was ascertained. A more robust and fully automatic segmentation method such as [157], [158], or [159] might have increased the accuracy of our results further.…”

Section: Discussionmentioning

confidence: 88%

“…Automated quantitative segmentation methods are suitable for assessing impact of WML on cognitive function [155]. Lately, several approaches for automatic segmentation of WML have been suggested [158,157,159]. A contribution of this thesis has been to use automatic segmentation routines for the calculation of total and regional volume of WML.…”

Section: Quantitative Analysis Of Wml In Dementiamentioning

confidence: 99%

“…Manual editing is also prone to subjectivity and low intra-and inter-correlation coefficient. In future research we would recommend applying a completely automatic WML segmentation procedure such as , [158], [157], or [159] to ensure a reliable segmentation result.…”

Section: Wml Segmentationmentioning

confidence: 99%

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Classifying Alzheimer's disease, Lewy body dementia, and normal controls using 3D texture analysis in magnetic resonance images

Oppedal

Engan

Eftestøl

et al. 2017

Biomedical Signal Processing and Control

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IntroductionExpected age is increasing globally and dementia is a common outcome for an increasing number of people. Dementia is a demanding syndrome for the patient and the environment as well as it is costly for society. Damaging changes to the cerebral blood flow also called white matter lesions (WML) are common in the elderly and is expected to increase as age advances. It has been reported that these types of lesions affect cognition in healthy elderly. They are also associated to Alzheimer's disease but have not been much studied in DLB. Quantitative analysis and machine learning have a potential to contribute in understanding the disease process as well as aid in diagnosis. MethodsQuantitative analysis of WML volumes were calculated using an automatic segmentation routine on magnetic resonance images (MRI) of subjects with Alzheimer's disease (AD), Lewy body dementia (LBD), and normal controls (NC). Statistical tests were performed to compare groups as well as to investigate relations to cognition. Additionally, WML volumes were used as features in a machine learning (ML) environment to check whether WML volume were able to classify subjects with AD and LBD from NC. Texture analysis (TA) may be able to document changes at a microstructural level and was performed in WML an non-WML regions of the different types of MRI's (FLAIR and T1). 2D-and 3D TA features were calculated and used in classification with the aim to serve as a tool for computer aided diagnosis (CAD) in dementia. The dataset used was imbalanced meaning that the number of subjects in each group were very different. Two methods for handling the imbalanced data were tested, namely upsampling and cost-sensitive classification.v Results and conclusionsSeverity of WML did neither differ significantly between subjects with dementia and NC nor between mildly demented patients with AD and LBD. WML severity were associated with cognitive decline in AD, but not LBD suggesting that WML contributes to cognitive decline in AD, but not LBD. More studies of the potential clinical impact of WML in patients with LBD are needed.The best classification results obtained using WML volumes as features in an ML framework discerning subjects with dementia from healthy controls were an area under curve (AUC) of 0.73 and 95% confidence interval of 0.57 to 0.83.We experienced better classification results when using TA features compared to WML volumes in classification and better results when performing classification on TA features calculated from T1 MRI compared to FLAIR MRI. A total accuracy, reported as mean with standard deviation in brackets over cross validation folds, of 0.97(0.07) or higher was reported for the dementia vs. NC, AD vs. NC, and LBD vs. NC classification problems for both the 2D-and 3D texture analysis approaches. In the AD vs. LBD case a total accuracy of 0.73(0.16) was reported using the 2D TA approach slightly exceeded by the 3D TA approach were 0.79(0.15) was reported.It seems like the results do not differ much when performing anal...

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Section: Discussionmentioning

confidence: 88%

Section: Quantitative Analysis Of Wml In Dementiamentioning

confidence: 99%

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Classifying Alzheimer's disease, Lewy body dementia, and normal controls using 3D texture analysis in magnetic resonance images

Oppedal

Engan

Eftestøl

et al. 2017

Biomedical Signal Processing and Control

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“…For Cascade, optimal performance was assured by running the segmentation algorithm on the training data while sweeping the two main parameters (α={0.05, 0.10, …, 1.00} and LPV threshold={α, α-0.05, …, 0}). 10,11 The parameter combination that provided the group-wise highest average spatial overlap was considered as the optimal configuration. A similar approach was used to derive the optimal configuration for LPA, sweeping κ={0.05, 0.10, …, 1.00}…”

Section: Parameter Tuningmentioning

confidence: 99%

Performance of five research-domain automated WM lesion segmentation methods in a multi-center MS study

Sitter

Steenwijk²,

Ruet

et al. 2017

NeuroImage

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Background and Purpose: In vivo identification of white matter lesions plays a key-role in evaluation of patients with multiple sclerosis (MS). Automated lesion segmentation methods have been developed to substitute manual outlining, but evidence of their performance in multi-center investigations is lacking. In this work, five research-domain automated segmentation methods were evaluated using a multi-center MS dataset. Positive (FP) volumes and False Negative (FN) volumes were used to examine spatial agreement. All analyses were repeated using a leave-one-center-out design to exclude the center of interest from the training phase to evaluate the performance of the method on 'unseen' center.

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“…A estratégia sugerida neste problema de classificação seria o uso dos dados de todas as técnicas quantitativas aqui discutidas provenientes das regiões envolvidas na classificação restrita conjuntamente com o precuneus e o fusiform, aplicando o KNN5 com seleção de melhores atributos. BASIR, 2005;DAMANGIR et al, 2012;BENDFELDT et al, 2012). No tema específico de Esclerose Múltipla existem alguns trabalhos que utilizam técnicas de reconhecimento de padrões para análise das lesões (KAMBER et al, 1995;LAO et al, 2008;GEREMIA et al, 2011), melhorando o desempenho das classificações.…”

Section: Ao Departamento De Unidade De Pesquisa Clínica Do Hospital Dunclassified

Aplicação de técnicas de reconhecimento de padrões em dados quantitativos de neuroimagens por ressonância magnética em pacientes de esclerose múltipla

Pessini¹

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Multispectral MRI segmentation of age related white matter changes using a cascade of support vector machines

Cited by 42 publications

References 26 publications

Classifying Alzheimer's disease, Lewy body dementia, and normal controls using 3D texture analysis in magnetic resonance images

Classifying Alzheimer's disease, Lewy body dementia, and normal controls using 3D texture analysis in magnetic resonance images

Performance of five research-domain automated WM lesion segmentation methods in a multi-center MS study

Aplicação de técnicas de reconhecimento de padrões em dados quantitativos de neuroimagens por ressonância magnética em pacientes de esclerose múltipla

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