29 research outputs found
Analysis of Sub-Cortical Morphology in Benign Epilepsy with Centrotemporal Spikes
RÉSUMÉ
Au Canada, l’épilepsie affecte environ 5 à 8 enfants par 3222 âgés de 2 à 37 ans dans la population globale. Quinze à 47 % de ces enfants ont une épilepsie bénigne avec des pointes centrotemporelles (BECTS), ce qui fait de BECTS le syndrome épileptique focal de l’enfant bénin le plus fréquent. Initialement, BECTS était considéré comme bénin parmi les autres épilepsies car il était généralement rapporté que les capacités cognitives ont été préservées
ou ramenées à la normale pendant la rémission. Cependant, certaines études ont trouvé des déficits cognitifs et comportementaux, qui peuvent bien persister même après la rémission.
Compte tenu des différences neurocognitives chez les enfants atteints de BECTS et de témoins normaux, la question est de savoir si des variations morphométriques subtiles dans les structures cérébrales sont également présentes chez ces patients et si elles expliquent des
variations dans les performence cognitifs. En fait, malgré les preuves accumulées d’une étiologie
neurodéveloppementale dans le BECTS, peu est connu sur les altérations structurelles sous-jacentes. À cet égard, la proposition de méthodes avancées en neuroimagerie permettrait d’évaluer quantitativement les variations de la morphologie cérébrale associées à ce trouble neurologique. En outre, l’étude du développement morphologique du cerveau et sa relation avec la cognition peut aider à élucider la base neuroanatomique des déficits cognitifs. Le but
de cette thèse est donc de fournir un ensemble d’outils pour analyser les variations morphologiques sous-corticales subtiles provoquées par différentes maladies, telles que l’épilepsie bénigne avec des pointes centrotemporelles.
La méthodologie adoptée dans cette thèse a conduit à trois objectifs de recherche spécifiques.
La première étape vise à développer un nouveau cadre automatisé pour segmenter les structures sous-corticales sur les images à resonance magnètique (IRM). La deuxième étape vise à concevoir une nouvelle approche basée sur la correspondance spectrale pour capturer précisément la variabilité de forme chez les sujets épileptiques. La troisième étape conduit à une analyse de la relation entre les changements morphologiques du cerveau et les indices
cognitifs.
La première contribution vise plus spécifiquement la segmentation automatique des structures sous-corticales dans un processus de co-recalage et de co-segmentation multi-atlas. Contrairement aux approches standards de segmentation multi-atlas, la méthode proposée obtient la segmentation finale en utilisant un recalage en fonction de la population, tandis que les connaissances à prior basés sur les réseaux neuronaux par convolution (CNNs) sont
incorporées dans la formulation d’énergie en tant que représentation d’image discriminative.
Ainsi, cette méthode exploite des représentations apprises plus sophistiquées pour conduire le processus de co-recalage. De plus, étant donné un ensemble de volumes cibles, la méthode proposée calcule les probabilités de segmentation individuellement, puis segmente tous les
volumes simultanément. Par conséquent, le fardeau de fournir un sous-ensemble de vérité connue approprié pour effectuer la segmentation multi-atlas est évité. Des résultats prometteurs démontrent le potentiel de notre méthode sur deux ensembles de données, contenant des annotations de structures sous-corticales. L’importance des estimations fiables des annotations est également mise en évidence, ce qui motive l’utilisation de réseaux neuronaux
profonds pour remplacer les annotations de vérité connue en co-recalage avec une perte de performance minimale.
La deuxième contribution vise à saisir la variabilité de forme entre deux populations de surfaces en utilisant une analyse morphologique multijoints. La méthode proposée exploite la représentation spectrale pour établir des correspondances de surface, puisque l’appariement est plus simple dans le domaine spectral plutôt que dans l’espace euclidien conventionnel.
Le cadre proposé intègre la concordance spectrale à courbure moyenne dans un plateforme d’analyse de formes sous-corticales multijoints. L’analyse expérimentale sur des données cliniques a montré que les différences de groupe extraites étaient similaires avec les résultats
dans d’autres études cliniques, tandis que les sorties d’analyse de forme ont été créées d’une manière à réduire le temps de calcul.
Enfin, la troisième contribution établit l’association entre les altérations morphologiques souscorticales
chez les enfants atteints d’épilepsie bénigne et les indices cognitifs. Cette étude permet de détecter les changements du putamen et du noyau caudé chez les enfants atteints de BECTS gauche, droit ou bilatéral. De plus, l ’association des différences volumétriques structurelles
et des différences de forme avec la cognition a été étudiée. Les résultats confirment les altérations de la forme du putamen et du noyau caudé chez les enfants atteints de BECTS.
De plus, nos résultats suggèrent que la variation de la forme sous-corticale affecte les fonctions cognitives. Cette étude démontre que les altérations de la forme et leur relation avec la cognition dépendent du côté de la focalisation de l’épilepsie.
Ce projet nous a permis d’étudier si de nouvelles méthodes permettraient de traiter automatiquement les informations de neuro-imagerie chez les enfants atteints de BECTS et de
détecter des variations morphologiques subtiles dans leurs structures sous-corticales. De plus, les résultats obtenus dans le cadre de cette thèse nous ont permis de conclure qu’il existe une association entre les variations morphologiques et la cognition par rapport au côté de la
focalisation de la crise épileptique.----------ABSTRACT
In Canada, epilepsy affects approximately 5 to 8 children per 3222 aged from 2 to 37 years in the overall population. Fifteen to 47% of these children have benign epilepsy with centrotemporal spikes (BECTS), making BECTS the most common benign childhood focal epileptic syndrome. Initially, BECTS was considered as benign among other epilepsies since it was
generally reported that cognitive abilities were preserved or brought back to normal during remission. However, some studies have found cognitive and behavioral deficits, which
may well persist even after remission. Given neurocognitive differences among children with BECTS and normal controls, the question is whether subtle morphometric variations in brain structures are also present in these patients, and whether they explain variations in cognitive indices. In fact, despite the accumulating evidence of a neurodevelopmental etiology in BECTS, little is known about underlying structural alterations. In this respect, proposing advanced neuroimaging methods will allow for quantitative assessment of variations in brain morphology associated with this neurological disorder. In addition, studying the brain morphological development and its relationship with cognition may help elucidate the neuroanatomical basis of cognitive deficits. Therefore, the focus of this thesis is to provide a set of tools for analyzing the subtle sub-cortical morphological alterations in different diseases, such as benign epilepsy with centrotemporal spikes.
The methodology adopted in this thesis led to addressing three specific research objectives. The first step develops a new automated framework for segmenting subcortical structures on MR images. The second step designs a new approach based on spectral correspondence to precisely capture shape variability in epileptic individuals. The third step finds the association between brain morphological changes and cognitive indices.
The first contribution aims more specifically at automatic segmentation of sub-cortical structures in a groupwise multi-atlas coregistration and cosegmentation process. Contrary to the standard multi-atlas segmentation approaches, the proposed method obtains the final segmentation using a population-wise registration, while Convolutional Neural Network (CNN)- based priors are incorporated in the energy formulation as a discriminative image representation. Thus, this method exploits more sophisticated learned representations to drive the
coregistration process. Furthermore, given a set of target volumes the developed method computes the segmentation probabilities individually, and then segments all the volumes simultaneously. Therefore, the burden of providing an appropriate ground truth subset to perform multi-atlas segmentation is removed. Promising results demonstrate the potential of our method on two different datasets, containing annotations of sub-cortical structures. The
importance of reliable label estimations is also highlighted, motivating the use of deep neural nets to replace ground truth annotations in coregistration with minimal loss in performance.
The second contribution intends to capture shape variability between two population of surfaces
using groupwise morphological analysis. The proposed method exploits spectral representation for establishing surface correspondences, since matching is simpler in the spectral
domain rather than in the conventional Euclidean space. The designed framework integrates mean curvature-based spectral matching in to a groupwise subcortical shape analysis pipeline.
Experimental analysis on real clinical dataset showed that the extracted group differences were in parallel with the findings in other clinical studies, while the shape analysis outputs were created in a computational efficient manner.
Finally, the third contribution establishes the association between sub-cortical morphological alterations in children with benign epilepsy and cognitive indices. This study detects putamen and caudate changes in children with left, right, or bilateral BECTS to age and gender matched healthy individuals. In addition, the association of structural volumetric and shape differences with cognition is investigated. The findings confirm putamen and caudate shape
alterations in children with BECTS. Also, our results suggest that variation in sub-cortical shape affects cognitive functions. More importantly, this study demonstrates that shape alterations and their relation with cognition depend on the side of epilepsy focus.
This project enabled us to investigate whether new methods would allow to automatically process neuroimaging information from children afflicted with BECTS and detect subtle morphological variations in their sub-cortical structures. In addition, the results obtained in this thesis allowed us to conclude the existence of the association between morphological variations and cognition with respect to the side of seizure focus
Sub-cortical brain structure segmentation using F-CNN's
In this paper we propose a deep learning approach for segmenting sub-cortical
structures of the human brain in Magnetic Resonance (MR) image data. We draw
inspiration from a state-of-the-art Fully-Convolutional Neural Network (F-CNN)
architecture for semantic segmentation of objects in natural images, and adapt
it to our task. Unlike previous CNN-based methods that operate on image
patches, our model is applied on a full blown 2D image, without any alignment
or registration steps at testing time. We further improve segmentation results
by interpreting the CNN output as potentials of a Markov Random Field (MRF),
whose topology corresponds to a volumetric grid. Alpha-expansion is used to
perform approximate inference imposing spatial volumetric homogeneity to the
CNN priors. We compare the performance of the proposed pipeline with a similar
system using Random Forest-based priors, as well as state-of-art segmentation
algorithms, and show promising results on two different brain MRI datasets.Comment: ISBI 2016: International Symposium on Biomedical Imaging, Apr 2016,
Prague, Czech Republi
Proactive admission control and dynamic resource management in SDN-based virtualized networks
Network virtualization is a promising approach in which common physical resources are shared between service
providers. Due to the substrate network limitations such as maximum
available memory of each node of the substrate network as well as different service priorities and requirements, resource management in this setup is essential. On the other hand, SDN
is bringing a considerable flexibility in resource management by introducing a centralized controller which can monitor all the substrate network states. In this paper, we propose a proactive admission control and dynamic resource management in SDNbased virtualized network in which the number of accepted highpriority virtual network (VN) requests is maximized, subject to both substrate limitations and memory requirement of each VN request. In the proposed formulation, based on the prediction of the substrate network utilization, we reserve resources for
upcoming high-priority VN requests. Via simulation, we show that the algorithm can increase the acceptance ratio of the highpriority VN requests up to % 100 where the substrate network is congested, i.e., arrival rates of both high-priority and low-priority VN requests are high
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Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021
BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation
A Framework for Detecting System Performance Anomalies Using Tracing Data Analysis
Advances in technology and computing power have led to the emergence of complex and large-scale software architectures in recent years. However, they are prone to performance anomalies due to various reasons, including software bugs, hardware failures, and resource contentions. Performance metrics represent the average load on the system and do not help discover the cause of the problem if abnormal behavior occurs during software execution. Consequently, system experts have to examine a massive amount of low-level tracing data to determine the cause of a performance issue. In this work, we propose an anomaly detection framework that reduces troubleshooting time, besides guiding developers to discover performance problems by highlighting anomalous parts in trace data. Our framework works by collecting streams of system calls during the execution of a process using the Linux Trace Toolkit Next Generation(LTTng), sending them to a machine learning module that reveals anomalous subsequences of system calls based on their execution times and frequency. Extensive experiments on real datasets from two different applications (e.g., MySQL and Chrome), for varying scenarios in terms of available labeled data, demonstrate the effectiveness of our approach to distinguish normal sequences from abnormal ones