Identification of equivalent topography in an open channel flow using Lagrangian data assimilation

International audienceWe present a Lagrangian data assimilation experiment in an open channel flow above a broad-crested weir. The observations consist of trajectories of particles transported by the flow and extracted from a video film, in addition to classical water level measurements. However, the presence of vertical recirculations on both sides of the weir actually conducts to the identification of an equivalent topography corresponding to the lower limit of a surface jet. In addition, results on the identification of the Manning coefficient may allow to detect the presence of bottom recirculations

Postmortem Brain Imaging in Alzheimer\u27s Disease and Related Dementias: The South Texas Alzheimer\u27s Disease Research Center Repository

Background: Neuroimaging bears the promise of providing new biomarkers that could refine the diagnosis of dementia. Still, obtaining the pathology data required to validate the relationship between neuroimaging markers and neurological changes is challenging. Existing data repositories are focused on a single pathology, are too small, or do not precisely match neuroimaging and pathology findings. Objective: The new data repository introduced in this work, the South Texas Alzheimer’s Disease research center repository, was designed to address these limitations. Our repository covers a broad diversity of dementias, spans a wide age range, and was specifically designed to draw exact correspondences between neuroimaging and pathology data. Methods: Using four different MRI sequences, we are reaching a sample size that allows for validating multimodal neuroimaging biomarkers and studying comorbid conditions. Our imaging protocol was designed to capture markers of cerebrovascular disease and related lesions. Quantification of these lesions is currently underway with MRI-guided histopathological examination. Results: A total of 139 postmortem brains (70 females) with mean age of 77.9 years were collected, with 71 brains fully analyzed. Of these, only 3% showed evidence of AD-only pathology and 76% had high prevalence of multiple pathologies contributing to clinical diagnosis. Conclusion: This repository has a significant (and increasing) sample size consisting of a wide range of neurodegenerative disorders and employs advanced imaging protocols and MRI-guided histopathological analysis to help disentangle the effects of comorbid disorders to refine diagnosis, prognosis and better understand neurodegenerative disorders

Segmentation et suivi de structures curvilinéaires en imagerie interventionnelle

This thesis addresses the segmentation and the tracking of thin curvilinear structures. The proposed methodology is applied to the delineation and the tracking of the guide-wires that are used during cardiac angioplasty. During these interventions, cardiologists assess the displacement of the different devices with a real-time fluoroscopic imaging system. The obtained images are very noisy and, as a result, guide-wires are particularly challenging to segment and track. The contributions of this thesis can be grouped into three parts. The first part is devoted to the detection of the guide-wires, the second part addresses their segmentation and the last part focuses on their spatio-temporal tracking. Partial detection of guide-wires is addressed either through the selection of appropriate filter operators or using modern machine learning methods. First, a learning framework using an asymmetric Boosting algorithm for training a guidewire detector is presented. A second method enhancing the output of a steerable filter by using an efficient tensor voting variant is then described. In the second part, a bottom-up method is proposed, that consists in grouping points selected by the wire detector, in extracting primitives from these aggregates and in linking these primitives together. Two local grouping procedures are investigated: one based on unsupervised graph-based clustering followed by a linesegment extraction and one based on a graphical model formulation followed by a graph-based centerline extraction. Subsequently, two variants of linking methods are investigated: one is based on integer programming and one on a local search heuristic. In the last part, registration methods are exploited for improving the segmentation via an image fusion method and then for tracking the wires. This latter is performed by a graph-based iconic tracking method coupled with a graphbased geometric tracking that encodes to certain extend a predictive model. This method uses a coupled graphical model that seeks both optimal position (segmentation) and spatio-temporal correspondences (tracking). The optimal solution of this graphical model simultaneously determines the guide-wire displacements and matches the landmarks that are extracted along it, what provides a robust estimation of the wire deformations with respect to large motion and noise.Cette thèse traite de la segmentation et du suivi de structures curvilinéaires. La méthodologie proposée est appliquée à la segmentation et au suivi des guide-fils durant les interventions d’angioplastie. Pendant ces opérations, les cardiologues s’assurent que le positionnement des différents outils est correct au moyen d’un système d’imagerie fluoroscopique temps-réel. Les images obtenues sont très bruitées et les guides sont, en conséquence, particulièrement difficiles à segmenter. Les contributions de cette thèse peuvent être regroupées en trois parties. La première est consacrée à la détection des guides, la seconde a leur segmentation et la dernière a leur suivi. La détection partielle des guide-fils est réalisée soit par la sélection d’un opérateur de filtrage approprié soit en utilisant des méthodes modernes d’apprentissage artificiel. Dans un premier temps, un système réalisant un Boosting asymétrique pour entraîner un détecteur de guides est présenté. Par la suite, une méthode renforçant la réponse d’un filtre orientable au moyen d’une variante efficace de vote tensoriel est décrite. Dans la seconde partie, une approche ascendante est proposée, qui consiste à regrouper des points sélectionnés par le détecteur de fil, à extraire des primitives des agrégats obtenus et a les lier. Deux procédures locales de regroupement des points sont étudiées : une reposant sur un clustering de graphe non supervisé suivi d’une extraction de segments de droites ; et l’autre reposant sur un modèle graphique puis une extraction d’axe central. Par la suite, deux méthodes de liaison des primitives sont étudiées : la première repose sur une approche de programmation linéaire, et la seconde sur une heuristique de recherche locale. Dans la dernière partie, des méthodes de recalage sont utilisées pour améliorer la segmentation et pour suivre les fils. Le suivi propos´e couple un suivi iconique avec un suivi géométrique contenant un modèle prédictif. Cette méthode utilise un modèle graphique déterminant à la fois une position du guide-fil (segmentation) et des correspondances (tracking). La solution optimale de ce modèle graphique décrit simultanément les déplacements du guide-fil et les appariements entre points d’intérêt qui en sont extraits, fournissant ainsi une estimation robuste des déformations du fil par rapport aux grands déplacements et au bruit

Guide-Wire Extraction through Perceptual Organization of Local Segments in Fluoroscopic Images

International audienceSegmentation of surgical devices in fluoroscopic images and in particular of guide-wires is a valuable element during surgery. In cardiac angioplasty, the problem is particularly challenging due to the following reasons: (i) low signal to noise ratio, (ii) the use of 2D images that accumulate information from the whole volume, and (iii) the similarity between the structure of interest and adjacent anatomical structures. In this paper we propose a novel approach to address these challenges, that combines efficiently low-level detection using machine learning techniques, local unsupervised clustering detections and finally high-level perceptual organization of these segments towards its complete reconstruction. The latter handles miss-detections and is based on a local search algorithm. Very promising results were obtained