Comparison of Energy Minimization Methods for 3-D Brain Tissue Classification

This paper presents 3-D brain tissue classificationschemes using three recent promising energy minimizationmethods for Markov random fields: graph cuts, loopybelief propagation and tree-reweighted message passing.The classification is performed using the well knownfinite Gaussian mixture Markov Random Field model.Results from the above methods are compared with widelyused iterative conditional modes algorithm. Theevaluation is performed on a dataset containing simulatedT1-weighted MR brain volumes with varying noise andintensity non-uniformities. The comparisons are performedin terms of energies as well as based on ground truthsegmentations, using various quantitative metrics.

MBIS: multivariate Bayesian image segmentation tool.

We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multichannel bias field correction based on a B-spline model. A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model. Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data. We first validate the accuracy of segmentation and the estimated bias field for each channel. MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation. The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan-rescan protocols on 21 healthy subjects. Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images. Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects. This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge.

Weighted Shape-based Averaging with Neighborhood Prior Model for Multiple Atlas Fusion-based Medical Image Segmentation

In medical imaging, merging automated segmentations obtained from multiple atlases has become a standard practice for improving the accuracy. In this letter, we propose two new fusion methods: "Global Weighted Shape-Based Averaging" (GWSBA) and "Local Weighted Shape-Based Averaging" (LWSBA). These methods extend the well known Shape-Based Averaging (SBA) by additionally incorporating the similarity information between the reference (i.e., atlas) images and the target image to be segmented. We also propose a new spatially-varying similarity-weighted neighborhood prior model, and an edge-preserving smoothness term that can be used with many of the existing fusion methods. We first present our new Markov Random Field (MRF) based fusion framework that models the above mentioned information. The proposed methods are evaluated in the context of segmentation of lymph nodes in the head and neck 3D CT images, and they resulted in more accurate segmentations compared to the existing SBA.

Brain tissue segmentation of fetal MR images

We present a segmentation method for fetal brain tissuesof T2w MR images, based on the well known ExpectationMaximization Markov Random Field (EM- MRF) scheme. Ourmain contribution is an intensity model composed of 7Gaussian distribution designed to deal with the largeintensity variability of fetal brain tissues. The secondmain contribution is a 3-steps MRF model that introducesboth local spatial and anatomical priors given by acortical distance map. Preliminary results on 4 subjectsare presented and evaluated in comparison to manualsegmentations showing that our methodology cansuccessfully be applied to such data, dealing with largeintensity variability within brain tissues and partialvolume (PV).

Brain Surface Segmentation of Magnetic Resonance Images of the Fetus

In this work we present a method for the image analysisof Magnetic Resonance Imaging (MRI) of fetuses. Our goalis to segment the brain surface from multiple volumes(axial, coronal and sagittal acquisitions) of a fetus. Tothis end we propose a two-step approach: first, a FiniteGaussian Mixture Model (FGMM) will segment the image into3 classes: brain, non-brain and mixture voxels. Second, aMarkov Random Field scheme will be applied tore-distribute mixture voxels into either brain ornon-brain tissue. Our main contributions are an adaptedenergy computation and an extended neighborhood frommultiple volumes in the MRF step. Preliminary results onfour fetuses of different gestational ages will be shown.

Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations.

Brain fluctuations at rest are not random but are structured in spatial patterns of correlated activity across different brain areas. The question of how resting-state functional connectivity (FC) emerges from the brain's anatomical connections has motivated several experimental and computational studies to understand structure-function relationships. However, the mechanistic origin of resting state is obscured by large-scale models' complexity, and a close structure-function relation is still an open problem. Thus, a realistic but simple enough description of relevant brain dynamics is needed. Here, we derived a dynamic mean field model that consistently summarizes the realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network, in which connectivity is constrained by diffusion imaging data from human subjects. The dynamic mean field approximates the ensemble dynamics, whose temporal evolution is dominated by the longest time scale of the system. With this reduction, we demonstrated that FC emerges as structured linear fluctuations around a stable low firing activity state close to destabilization. Moreover, the model can be further and crucially simplified into a set of motion equations for statistical moments, providing a direct analytical link between anatomical structure, neural network dynamics, and FC. Our study suggests that FC arises from noise propagation and dynamical slowing down of fluctuations in an anatomically constrained dynamical system. Altogether, the reduction from spiking models to statistical moments presented here provides a new framework to explicitly understand the building up of FC through neuronal dynamics underpinned by anatomical connections and to drive hypotheses in task-evoked studies and for clinical applications.

A pipeline approach with spatial information for segmenting multiple sclerosis lesions on brain magnetic resonance imaging

Background: Conventional magnetic resonance imaging (MRI) techniques are highly sensitive to detect multiple sclerosis (MS) plaques, enabling a quantitative assessment of inflammatory activity and lesion load. In quantitative analyses of focal lesions, manual or semi-automated segmentations have been widely used to compute the total number of lesions and the total lesion volume. These techniques, however, are both challenging and time-consuming, being also prone to intra-observer and inter-observer variability.Aim: To develop an automated approach to segment brain tissues and MS lesions from brain MRI images. The goal is to reduce the user interaction and to provide an objective tool that eliminates the inter- and intra-observer variability.Methods: Based on the recent methods developed by Souplet et al. and de Boer et al., we propose a novel pipeline which includes the following steps: bias correction, skull stripping, atlas registration, tissue classification, and lesion segmentation. After the initial pre-processing steps, a MRI scan is automatically segmented into 4 classes: white matter (WM), grey matter (GM), cerebrospinal fluid (CSF) and partial volume. An expectation maximisation method which fits a multivariate Gaussian mixture model to T1-w, T2-w and PD-w images is used for this purpose. Based on the obtained tissue masks and using the estimated GM mean and variance, we apply an intensity threshold to the FLAIR image, which provides the lesion segmentation. With the aim of improving this initial result, spatial information coming from the neighbouring tissue labels is used to refine the final lesion segmentation.Results:The experimental evaluation was performed using real data sets of 1.5T and the corresponding ground truth annotations provided by expert radiologists. The following values were obtained: 64% of true positive (TP) fraction, 80% of false positive (FP) fraction, and an average surface distance of 7.89 mm. The results of our approach were quantitatively compared to our implementations of the works of Souplet et al. and de Boer et al., obtaining higher TP and lower FP values.Conclusion: Promising MS lesion segmentation results have been obtained in terms of TP. However, the high number of FP which is still a well-known problem of all the automated MS lesion segmentation approaches has to be improved in order to use them for the standard clinical practice. Our future work will focus on tackling this issue.

Validation of tissue modelization and classification techniques in T1-weighted MR brain images

We propose a deep study on tissue modelization andclassification Techniques on T1-weighted MR images. Threeapproaches have been taken into account to perform thisvalidation study. Two of them are based on FiniteGaussian Mixture (FGM) model. The first one consists onlyin pure gaussian distributions (FGM-EM). The second oneuses a different model for partial volume (PV) (FGM-GA).The third one is based on a Hidden Markov Random Field(HMRF) model. All methods have been tested on a DigitalBrain Phantom image considered as the ground truth. Noiseand intensity non-uniformities have been added tosimulate real image conditions. Also the effect of ananisotropic filter is considered. Results demonstratethat methods relying in both intensity and spatialinformation are in general more robust to noise andinhomogeneities. However, in some cases there is nosignificant differences between all presented methods.

On the convergence of EM-like algorithms for image segmentation using Markov random fields.

Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation-maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad hoc and may turn out numerically unstable. In this paper, we review three EM-like variants for Markov random field segmentation and compare their convergence properties both at the theoretical and practical levels. We specifically advocate a numerical scheme involving asynchronous voxel updating, for which general convergence results can be established. Our experiments on brain tissue classification in magnetic resonance images provide evidence that this algorithm may achieve significantly faster convergence than its competitors while yielding at least as good segmentation results.

Fusion of Multi-Atlas Segmentations with Spatial Distribution Modeling

In recent years, multi-atlas fusion methods have gainedsignificant attention in medical image segmentation. Inthis paper, we propose a general Markov Random Field(MRF) based framework that can perform edge-preservingsmoothing of the labels at the time of fusing the labelsitself. More specifically, we formulate the label fusionproblem with MRF-based neighborhood priors, as an energyminimization problem containing a unary data term and apairwise smoothness term. We present how the existingfusion methods like majority voting, global weightedvoting and local weighted voting methods can be reframedto profit from the proposed framework, for generatingmore accurate segmentations as well as more contiguoussegmentations by getting rid of holes and islands. Theproposed framework is evaluated for segmenting lymphnodes in 3D head and neck CT images. A comparison ofvarious fusion algorithms is also presented.

Nonvitamin-k-antagonist oral anticoagulants in patients with atrial fibrillation and previous stroke or transient ischemic attack: a systematic review and meta-analysis of randomized controlled trials.

BACKGROUND AND PURPOSE: To assess whether the combined analysis of all phase III trials of nonvitamin-K-antagonist (non-VKA) oral anticoagulants in patients with atrial fibrillation and previous stroke or transient ischemic attack shows a significant difference in efficacy or safety compared with warfarin. METHODS: We searched PubMed until May 31, 2012, for randomized clinical trials using the following search items: atrial fibrillation, anticoagulation, warfarin, and previous stroke or transient ischemic attack. Studies had to be phase III trials in atrial fibrillation patients comparing warfarin with a non-VKA currently on the market or with the intention to be brought to the market in North America or Europe. Analysis was performed on intention-to-treat basis. A fixed-effects model was used as more appropriate than a random-effects model when combining a small number of studies. RESULTS: Among 47 potentially eligible articles, 3 were included in the meta-analysis. In 14 527 patients, non-VKAs were associated with a significant reduction of stroke/systemic embolism (odds ratios, 0.85 [95% CI, 074-0.99]; relative risk reduction, 14%; absolute risk reduction, 0.7%; number needed to treat, 134 over 1.8-2.0 years) compared with warfarin. Non-VKAs were also associated with a significant reduction of major bleeding compared with warfarin (odds ratios, 0.86 [95% CI, 075-0.99]; relative risk reduction, 13%; absolute risk reduction, 0.8%; number needed to treat, 125), mainly driven by the significant reduction of hemorrhagic stroke (odds ratios, 0.44 [95% CI, 032-0.62]; relative risk reduction, 57.9%; absolute risk reduction, 0.7%; number needed to treat, 139). CONCLUSIONS: In the context of the significant limitations of combining the results of disparate trials of different agents, non-VKAs seem to be associated with a significant reduction in rates of stroke or systemic embolism, hemorrhagic stroke, and major bleeding when compared with warfarin in patients with previous stroke or transient ischemic attack.

Stability and change in party preferences.

This article analyses stability and volatility of party preferences using data from the Swiss Household-Panel (SHP), which, for the first time, allow studying transitions and stability of voters over several years in Switzerland. Analyses cover the years 1999- 2007 and systematically distinguish changes between party blocks and changes within party blocks. The first part looks at different patterns of change, which show relatively high volatility. The second part tests several theories on causes of such changes applying a multinomial random-effects model. Results show that party preferences stabilise with their duration and with age and that the electoral cycle, political sophistication, socio-structural predispositions, the household-context as well as party size and the number of parties each explain part of electoral volatility. Different results for withinand between party-block changes underlie the importance of that differentiation.

Assessing the Relationship between the Baseline Value of a Continuous Variable and Subsequent Change over Time

Analyzing the relationship between the baseline value and subsequent change of a continuous variable is a frequent matter of inquiry in cohort studies. These analyses are surprisingly complex, particularly if only two waves of data are available. It is unclear for non-biostatisticians where the complexity of this analysis lies and which statistical method is adequate.With the help of simulated longitudinal data of body mass index in children,we review statistical methods for the analysis of the association between the baseline value and subsequent change, assuming linear growth with time. Key issues in such analyses are mathematical coupling, measurement error, variability of change between individuals, and regression to the mean. Ideally, it is better to rely on multiple repeated measurements at different times and a linear random effects model is a standard approach if more than two waves of data are available. If only two waves of data are available, our simulations show that Blomqvist's method - which consists in adjusting for measurement error variance the estimated regression coefficient of observed change on baseline value - provides accurate estimates. The adequacy of the methods to assess the relationship between the baseline value and subsequent change depends on the number of data waves, the availability of information on measurement error, and the variability of change between individuals.

Diagnostic Performance of Magnetic Resonance Imaging and Computed Tomography for Advanced Retinoblastoma: A Systematic Review and Meta-analysis.

PURPOSE: To determine and compare the diagnostic performance of magnetic resonance imaging (MRI) and computed tomography (CT) for the diagnosis of tumor extent in advanced retinoblastoma, using histopathologic analysis as the reference standard. DESIGN: Systematic review and meta-analysis. PARTICIPANTS: Patients with advanced retinoblastoma who underwent MRI, CT, or both for the detection of tumor extent from published diagnostic accuracy studies. METHODS: Medline and Embase were searched for literature published through April 2013 assessing the diagnostic performance of MRI, CT, or both in detecting intraorbital and extraorbital tumor extension of retinoblastoma. Diagnostic accuracy data were extracted from included studies. Summary estimates were based on a random effects model. Intrastudy and interstudy heterogeneity were analyzed. MAIN OUTCOME MEASURES: Sensitivity and specificity of MRI and CT in detecting tumor extent. RESULTS: Data of the following tumor-extent parameters were extracted: anterior eye segment involvement and ciliary body, optic nerve, choroidal, and (extra)scleral invasion. Articles on MRI reported results of 591 eyes from 14 studies, and articles on CT yielded 257 eyes from 4 studies. The summary estimates with their 95% confidence intervals (CIs) of the diagnostic accuracy of conventional MRI at detecting postlaminar optic nerve, choroidal, and scleral invasion showed sensitivities of 59% (95% CI, 37%-78%), 74% (95% CI, 52%-88%), and 88% (95% CI, 20%-100%), respectively, and specificities of 94% (95% CI, 84%-98%), 72% (95% CI, 31%-94%), and 99% (95% CI, 86%-100%), respectively. Magnetic resonance imaging with a high (versus a low) image quality showed higher diagnostic accuracies for detection of prelaminar optic nerve and choroidal invasion, but these differences were not statistically significant. Studies reporting the diagnostic accuracy of CT did not provide enough data to perform any meta-analyses. CONCLUSIONS: Magnetic resonance imaging is an important diagnostic tool for the detection of local tumor extent in advanced retinoblastoma, although its diagnostic accuracy shows room for improvement, especially with regard to sensitivity. With only a few-mostly old-studies, there is very little evidence on the diagnostic accuracy of CT, and generally these studies show low diagnostic accuracy. Future studies assessing the role of MRI in clinical decision making in terms of prognostic value for advanced retinoblastoma are needed.

Diagnostic Performances of 18F-FDG Positron Emission Tomography in Large Vessels Vasculitis: Meta-analysis

Toperform a meta-analysis of FDG-PET performances in the diagnosis of largevessels vasculitis (Giant Cell Arteritis (GCA) associated or not withPolymyalgia Rheumatica(PMR), Takayasu). Materials and methods : The MEDLINE,Cochrane Library, Embase were searched for relevant original articlesdescribing FDG-PET for vasculitis assessment, using MesH terms ("GiantCell Arteritis or Vasculitis" AND "PET"). Criteria for inclusionwere:(1)FDG-PET for diagnosis of vasculitis(2)American College of Rheumatologycriteria as reference standard(3)control group. After data extraction, analyseswere performed using a random-effects model. Results : Of 184 citations(database search and references screening),70 articles were reviewed of which12 eligible studies were extracted (sensitivity range from 32% to 97%). 7studies fulfilled all inclusion criteria. Owing to overlapping population, 1study was excluded. Statistical heterogeneity justified the random-effectsmodel. Pooled 6 studies analysis(116 vasculitis,224 controls) showed a 81%sensitivity (95%CI:70-89%);a 89% specificity (95%CI:77-95%);a 85%PPV(95%CI:63-95%); a 90% NPV(95%CI:79-95%);a 7.1 positive LR(95%CI:3.4-14.9); a0.2 negative LR(95%CI:0.14-0.35) and 90.1 DOR(95%CI: 18.6-437). Conclusion :FDG-PET has good diagnostic performances in the detection of large vesselsvasculitis. Its promising role could be extended to follow up patients undertreatment, but further studies are needed to confirm this possibility.