Automated model-based tissue classification of MR images of the brain

Leemput, Koen Van; Maes, Frederik; Vandermeulen, Dirk; Suetens, Paul

doi:10.1109/42.811270

Cited by 926 publications

(808 citation statements)

References 24 publications

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“…Furthermore, the initial hard classification and correction of intensity nonuniformity can be performed with other methods than those applied in this study. The state of the art algorithms for these tasks (such as Marroquin et al, 2002;Van Leemput et al, 1999;Zhang et al, 2001) are however more time consuming than the methods applied in this study. Moreover, the robustness in parameter estimation compensates also for classification errors as was shown with misregistered Brainweb images.…”

Section: Discussionmentioning

confidence: 99%

Fast and robust parameter estimation for statistical partial volume models in brain MRI

2004

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Due to the finite spatial resolution of imaging devices, a single voxel in a medical image may be composed of mixture of tissue types, an effect known as partial volume effect (PVE). Partial volume estimation, that is, the estimation of the amount of each tissue type within each voxel, has received considerable interest in recent years. Much of this work has been focused on the mixel model, a statistical model of PVE. We propose a novel trimmed minimum covariance determinant (TMCD) method for the estimation of the parameters of the mixel PVE model. In this method, each voxel is first labeled according to the most dominant tissue type. Voxels that are prone to PVE are removed from this labeled set, following which robust location estimators with high breakdown points are used to estimate the mean and the covariance of each tissue class. Comparisons between different methods for parameter estimation based on classified images as well as expectation -maximization-like (EM-like) procedure for simultaneous parameter and partial volume estimation are reported. The robust estimators based on a pruned classification as presented here are shown to perform well even if the initial classification is of poor quality. The results obtained are comparable to those obtained using the EM-like procedure, but require considerably less computation time. Segmentation results of real data based on partial volume estimation are also reported. In addition to considering the parameter estimation problem, we discuss differences between different approximations to the complete mixel model. In summary, the proposed TMCD method allows for the accurate, robust, and efficient estimation of partial volume model parameters, which is crucial to a variety of brain MRI data analysis procedures such as the accurate estimation of tissue volumes and the accurate delineation of the cortical surface. D 2004 Elsevier Inc. All rights reserved.

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Section: Discussionmentioning

confidence: 99%

Fast and robust parameter estimation for statistical partial volume models in brain MRI

2004

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“…First, an expectation maximization segmentation (EMS) algorithm including correction for intensity inhomogeneity (31, 32) was applied to the T1w image with supplementary T2w image input, to separate skull, scalp, extracranial tissue, cerebellum, and brain stem (at the level of the diencephalon) from the rest of brain volume. The remaining brain volume was voxel-wise classified into fractions of cerebral white matter (WM), cortical gray matter (GM), and sulcal cerebrospinal fluid (CSF).…”

Section: Methodsmentioning

confidence: 99%

Patterns of Reduced Cortical Thickness in Late-Life Depression and Relationship to Psychotherapeutic Response

Mackin

Tosun

Mueller

et al. 2013

The American Journal of Geriatric Psychiatry

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Objective Cortical atrophy has been associated with late life depression (LLD) and recent findings suggest that reduced right hemisphere cortical thickness is associated with familial risk for major depressive disorder but cortical thickness abnormalities in LLD have not been explored. Further, cortical atrophy has been posited as a contributor to poor antidepressant treatment response in LLD but the impact of cortical thickness on psychotherapy response is unknown. This study was conducted to evaluate patterns of cortical thickness in LLD and in relation to psychotherapy treatment outcomes. Methods Participants included 22 individuals with LLD and 12 age matched comparison subjects. LLD participants completed 12 weeks of psychotherapy and treatment response was defined as a 50% reduction in depressive symptoms. All participants participated in Magnetic Resonance Imaging (MRI) of the brain and cortical mapping of grey matter tissue thickness was calculated. Results LLD individuals demonstrated thinner cortex than controls prominently in the right frontal, parietal, and temporal brain regions. Eleven participants (50%) exhibited positive psychotherapy response after 12 weeks of treatment. Psychotherapy non-responders demonstrated thinner cortex in bilateral posterior cingulate and parahippocampal cortices, left paracentral, precuneus, cuneus, and insular cortices, and the right medial orbito-frontal and lateral occipital cortices relative to treatment responders. Conclusions Our findings suggest more distributed right hemisphere cortical abnormalities in LLD than have been previously reported. Additionally, our findings suggest that reduced bilateral cortical thickness may be an important phenotypic marker of individuals at higher risk for poor response to psychotherapy.

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“…Through the registration of an individual parcel, by matching the sulcal boundaries, its mesh gets stretched and deformed but the neighborhood of individual vertices is preserved and as a matter of consequence of the ROIs lying in that parcel. It is worth mentioning that prior to this operation the T1-weighted image had been registered onto the space of the diffusion data with an affine registration technique (Van Leemput et al, 1999).…”

Section: Cortical Registrationmentioning

confidence: 99%

Mapping the human connectome at multiple scales with diffusion spectrum MRI

Cammoun

Gigandet

Meskaldji

et al. 2012

Journal of Neuroscience Methods

445

525

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a b s t r a c tThe global structural connectivity of the brain, the human connectome, is now accessible at millimeter scale with the use of MRI. In this paper, we describe an approach to map the connectome by constructing normalized whole-brain structural connection matrices derived from diffusion MRI tractography at 5 different scales. Using a template-based approach to match cortical landmarks of different subjects, we propose a robust method that allows (a) the selection of identical cortical regions of interest of desired size and location in different subjects with identification of the associated fiber tracts (b) straightforward construction and interpretation of anatomically organized whole-brain connection matrices and (c) statistical inter-subject comparison of brain connectivity at various scales. The fully automated postprocessing steps necessary to build such matrices are detailed in this paper. Extensive validation tests are performed to assess the reproducibility of the method in a group of 5 healthy subjects and its reliability is as well considerably discussed in a group of 20 healthy subjects.

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Automated model-based tissue classification of MR images of the brain

Cited by 926 publications

References 24 publications

Fast and robust parameter estimation for statistical partial volume models in brain MRI

Fast and robust parameter estimation for statistical partial volume models in brain MRI

Patterns of Reduced Cortical Thickness in Late-Life Depression and Relationship to Psychotherapeutic Response

Mapping the human connectome at multiple scales with diffusion spectrum MRI

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