Two Time Point MS Lesion Segmentation in Brain MRI:An Expectation-Maximization Framework

Purpose: Lesion volume is a meaningful measure in multiple sclerosis (MS) prognosis. Manual lesion segmentation for computing volume in a single or multiple time points is time consuming and suffers from intra and inter-observer variability. Methods: In this paper, we present MSmetrix-long: a joint expectation-maximization (EM) framework for two time point white matter (WM) lesion segmentation. MSmetrix-long takes as input a 3D T1-weighted and a 3D FLAIR MR image and segments lesions in three steps: (1) cross-sectional lesion segmentation of the two time points; (2) creation of difference image, which is used to model the lesion evolution; (3) a joint EM lesion segmentation framework that uses output of step (1) and step (2) to provide the final lesion segmentation. The accuracy (Dice score) and reproducibility (absolute lesion volume difference) of MSmetrix-long is evaluated using two datasets. Results: On the first dataset, the median Dice score between MSmetrix-long and expert lesion segmentation was 0.63 and the Pearson correlation coefficient (PCC) was equal to 0.96. On the second dataset, the median absolute volume difference was 0.11 ml. Conclusions: MSmetrix-long is accurate and consistent in segmenting MS lesions. Also, MSmetrix-long compares favorably with the publicly available longitudinal MS lesion segmentation algorithm of Lesion Segmentation Toolbox

Pengaruh Brand Trust dan Brand Equity terhadap Loyalitas Konsumen pada Produk Kosmetik Wardah (Survey Konsumen pada PT. Paragon Technology And Innovation Cabang Pekanbaru)

The purpose of this study was to determine the influence of brand trust ( X1 ) and brand equity ( X2 ) customer loyalty ( Y ) in cosmetic products Wardah ( consumer survey on PT . Paragon technology and innovation branches pekanbaru ) . The method in this research is quantitatively using SPSS 21 program , where samples were used that consumers using cosmetic products Wardah by respondents as many as 100 people sampling technique is accidental sampling using the formula slovin . The results showed that the test results simultaneously obtained from the F count was 34.888 while the value of F table 3.090 . This means that F count> F table and significant value 0,000 < alpha of 0.05 . This means that brand trust and brand equity simultaneously significant effect on consumer loyalty to cosmetic products Wardah

Brain age as a biomarker for pathological versus healthy ageing – a REMEMBER study

Objectives: This study aimed to evaluate the potential clinical value of a new brain age prediction model as a single interpretable variable representing the condition of our brain. Among many clinical use cases, brain age could be a novel outcome measure to assess the preventive effect of life-style interventions. Methods: The REMEMBER study population (N = 742) consisted of cognitively healthy (HC,N = 91), subjective cognitive decline (SCD,N = 65), mild cognitive impairment (MCI,N = 319) and AD dementia (ADD,N = 267) subjects. Automated brain volumetry of global, cortical, and subcortical brain structures computed by the CE-labeled and FDA-cleared software icobrain dm (dementia) was retrospectively extracted from T1-weighted MRI sequences that were acquired during clinical routine at participating memory clinics from the Belgian Dementia Council. The volumetric features, along with sex, were combined into a weighted sum using a linear model, and were used to predict ‘brain age’ and ‘brain predicted age difference’ (BPAD = brain age–chronological age) for every subject. Results: MCI and ADD patients showed an increased brain age compared to their chronological age. Overall, brain age outperformed BPAD and chronological age in terms of classification accuracy across the AD spectrum. There was a weak-to-moderate correlation between total MMSE score and both brain age (r = -0.38,p < .001) and BPAD (r = -0.26,p < .001). Noticeable trends, but no significant correlations, were found between BPAD and incidence of conversion from MCI to ADD, nor between BPAD and conversion time from MCI to ADD. BPAD was increased in heavy alcohol drinkers compared to non-/sporadic (p = .014) and moderate (p = .040) drinkers. Conclusions: Brain age and associated BPAD have the potential to serve as indicators for, and to evaluate the impact of lifestyle modifications or interventions on, brain health

Probabilistic Framework for Population Analysis of Brain MR Images (Probabilistische raamwerk voor populatie analyse van MR hersenbeelden)

Many neurodegenerative diseases such as Alzheimer s disease can be characterized by their gradual modification of the cellular environment resulting in macroscopic brain changes. Groupwise analysis of large sets of magnetic resonance (MR) images, visualizing the morphology of the human brain, can provide reliable measurable image features indicative for a specific disease and/or disease stage. Such features, called biomarkers, can contribute to early diagnosis, to the assessment of therapy response as well as to the study of the underlying causes of the neurodegenerative disease. In this PhD, we developed a Bayesian framework for both cross-sectional and longitudinal groupwise analysis of a collection of brain MR images. Contrary to most state-of-the-art strategies for cross-sectional analysis, which compare homogeneous subgroups of images selected based on prior clinical knowledge, our framework can handle heterogeneous collections of images, for instance of both normal controls as well as patients in different disease states. The proposed framework simultaneously performs segmentation of each image into the major tissue classes (white matter, gray matter, CSF) and automatically clusters all images of the set in homogeneous subgroups based on the image morphology extracted from the image segmentations. A probabilistic brain atlas is iteratively constructed for each cluster within the framework. These atlases visualize the mean morphology of the subgroup and support the clustering and segmentation processes. The constructed morphological subgroups can be correlated with the clinical diagnosis of each subject, while the clustering process reveals the distinctive cluster-specific image features globally and for each individual image. The framework is adapted to take additional clinical knowledge into account in the clustering process if desired. The longitudinal framework is a direct extension of the cross-sectional framework, analyzing multi-temporal brain MR image sequences of different subjects suffering from the same neurodegenerative disease, aiming to assess both the patient-specific and the population-wise disease evolution based on the structural changes over time. The clustering process reveals the subject-specific disease evolution, while the constructed atlases form a longitudinal atlas representing the temporal changes in brain morphology caused by the disorder. A proof-of-concept of the framework is demonstrated on synthetic images and its feasibility is further shown on brain MR images from different publicly available data sets, including the simulated BrainWeb data set as well as the large international initiatives ADNI and OASIS. The experiments show that our framework provides accurate, more unbiased (and temporally consistent) image segmentations and that it can detect the clinically relevant morphological subgroups (cross-sectionally) or the individual disease progression (longitudinally) in a set of images or image sequences. The constructed atlases indicate the disease (stage) morphology better than traditional tools for cross-sectional and longitudinal analysis based on clinical prior knowledge. Finally, it is illustrated that our combinational approach for segmentation and clustering can find distinctive image features that would not have been picked up by handling both processes separately and subsequently. In conclusion, the presented method combines segmentation, clustering and atlas construction in a unified probabilistic framework such that all techniques can cooperate and such that the method becomes more data-driven. We provide initial evidence that our method facilitates a better representation of the disease evolution, the detection of novel subgroups and the detection of novel disease-specific features. Therefore, our method can become an important tool for the development of novel and refined imaging biomarkers, providing new insights in structural change and development.Ribbens A., ''Probabilistic framework for population analysis of brain MR images'', Proefschrift voorgedragen tot het behalen van het doctoraat in de ingenieurswetenschappen, KU Leuven, December 2012, Leuven, Belgium.status: publishe

Unified framework for automatic segmentation, probabilistic atlas construction, registration and clustering of brain MR images

Ribbens A., Maes F., Vandermeulen D., Suetens P., ''Unified framework for automatic segmentation, probabilistic atlas construction, registration and clustering of brain MR images'', 5th annual Leuven statistical day - LSD 2010, May 28, 2010, Heverlee, Belgium.status: publishe

Semisupervised probabilistic clustering of brain MR images including prior clinical information

Accurate morphologic clustering of subjects and detection of population specific differences in brain MR images, due to e.g. neurological diseases, is of great interest in medical image analysis. In previous work, we proposed a probabilistic framework for unsupervised image clustering that allows exposing cluster specific morphological differences in each image. In this paper, we extend this framework to also accommodate semisupervised clustering approaches which provides the possibility of including prior knowledge about cluster memberships, group-level morphological differences and clinical prior knowledge. The method is validated on three different data sets and a comparative study between the supervised, semisupervised and unsupervised methods is performed. We show that the use of a limited amount of prior knowledge about cluster memberships can contribute to a better clustering performance in certain applications, while on the other hand the semisupervised clustering is quite robust to incorrect prior clustering knowledge. © 2011 Springer-Verlag.Ribbens A., Maes F., Vandermeulen D., Suetens P., ''Semisupervised probabilistic clustering of brain MR images including prior clinical information'', Lecture notes in computer science, vol. 6533, pp. 184-194, 2011 (MICCAI 2010 workshop on medical computer vision : recognition techniques and applications in medical imaging - MCV 2010, September 20, 2010, Beijing, China).status: publishe

Analysis of a heterogeneous set of brain MR images by morphology-based clustering

Ribbens A., Maes F., Vandermeulen D., Suetens P., ''Analysis of a heterogeneous set of brain MR images by morphology-based clustering'', Special issue of frontiers in neuroinformatics, August 30, 2013 (INCF training in neuroinformatics 2013 - imaging the brain at multiple scales: how to integrate multi-scale structural information?, September 2-6, 2013, Antwerp, Belgium).status: publishe

Settlers of the brain: A board game to illustrate “Probabilistic framework for population analysis of brain MR images”

Ribbens A., Slagmolen P., Maes F., Suetens P., ''Settlers of the brain: A board game to illustrate “Probabilistic framework for population analysis of brain MR images”'', Knowledge for Growth - 9th edition of FlandersBio’s annual life sciences convention, May 30, 2013, Ghent, Belgium.status: publishe

An extensive validation study of non-rigid registration techniques for atlas based brain segmentation : local priors, bias field correction and parametric intensity model

Ribbens A., Maes F., Vandermeulen D., Suetens P., ''An extensive validation study of non-rigid registration techniques for atlas based brain segmentation : local priors, bias field correction and parametric intensity model'', Internal report KUL/ESAT/PSI/0904, K.U.Leuven, ESAT, March 2009, Leuven, Belgium.status: publishe

Assessing age-related gray matter decline with voxel-based morphometry depends significantly on segmentation and normalization procedures

Healthy ageing coincides with a progressive decline of brain gray matter (GM) ultimately affecting the entire brain. For a long time, manual delineation-based volumetry within predefined regions of interest (ROI) has been the gold standard for assessing such degeneration. Voxel-Based Morphometry (VBM) offers an automated alternative approach that, however, relies critically on the segmentation and spatial normalization of a large collection of images from different subjects. This can be achieved via different algorithms, with SPM5/SPM8, DARTEL of SPM8 and FSL tools (FAST, FNIRT) being three of the most frequently used. We complemented these voxel based measurements with a ROI based approach, whereby the ROIs are defined by transforms of an atlas (containing different tissue probability maps as well as predefined anatomic labels) to the individual subject images in order to obtain volumetric information at the level of the whole brain or within separate ROIs. Comparing GM decline between 21 young subjects (mean age 23) and 18 elderly (mean age 66) revealed that volumetric measurements differed significantly between methods. The unified segmentation/normalization of SPM5/SPM8 revealed the largest age-related differences and DARTEL the smallest, with FSL being more similar to the DARTEL approach. Method specific differences were substantial after segmentation and most pronounced for the cortical structures in close vicinity to major sulci and fissures. Our findings suggest that algorithms that provide only limited degrees of freedom for local deformations (such as the unified segmentation and normalization of SPM5/SPM8) tend to overestimate between-group differences in VBM results when compared to methods providing more flexible warping. This difference seems to be most pronounced if the anatomy of one of the groups deviates from custom templates, a finding that is of particular importance when results are compared across studies using different VBM methods.Callaert D.V., Ribbens A., Maes F., Swinnen S.P., Wenderoth N., ''Assessing age-related gray matter decline with voxel-based morphometry depends significantly on segmentation and normalization procedures'', Frontiers in aging neuroscience, vol. 6 (article124), 14 pp., June 23, 2014.status: publishe