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Joint Co-segmentation and Registration of 3D Ultrasound Images
No full textContrast-enhanced ultrasound (CEUS) allows a visualization of the vascularization and complements the anatomical information provided by conventional ultrasound (US). However, these images are inherently subject to noise and shadows, which hinders standard segmentation algorithms. In this paper, we propose to use simultaneously the different information coming from 3D US and CEUS images to address the problem of kidney segmentation. To that end, we introduce a generic framework for joint co-segmentation and registration that seeks objects having the same shape in several images. From this framework, we derive both an ellipsoid co-detection and a model-based co-segmentation algorithm. These methods rely on voxel-classification maps that we estimate using random forests in a structured way. This yields a fast and fully automated pipeline, in which an ellipsoid is first estimated to locate the kidney in both US and CEUS volumes and then deformed to segment it accurately. The proposed method outperforms state-of-the-art results (by dividing the kidney volume error by two) on a clinically representative database of 64 images.nonouirechercheInternationa
Sens, objets et stratégie en pratiques dans un projet immobilier
No full textLes pratiques de construction de sens sont au cœur de l’élaboration de la stratégie des organisations. Dans le cas d’une gestion par projets, cette construction collective de sens s’appuie sur l’utilisation d’objets. Les auteurs proposent ici une heuristique intégrative permettant d’analyser les pratiques à l’œuvre dans un projet immobilier. Leurs résultats contribuent à une meilleure connaissance des microfondations de la stratégie et du sensemaking.Sensemaking practices are a core activity for strategizing in organizations. Managers can use various types of objects to shape sense in a project. We propose an integrative framework to analyse the sensemaking practices at work in a real estate project. Our results provide a better understanding of the micro foundations of strategy and sensemaking.ouinonouirechercheNationa
Kidney detection and real-time segmentation in 3D contrast-enhanced ultrasound images
No full textIn this paper, we present an automatic method to segment the kidney in 3D contrast-enhanced ultrasound (CEUS) images. This modality has lately benefited of an increasing interest for diagnosis and intervention planning, as it allows to visualize blood flow in real-time harmlessly for the patient. Our method is composed of two steps: first, the kidney is automatically localized by a novel robust ellipsoid detector; then, segmentation is obtained through the deformation of this ellipsoid with a model-based approach. To cope with low image quality and strong organ variability induced by pathologies, the algorithm allows the user to refine the result by real-time interactions. Our method has been validated on a representative clinical database. (c) 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.nonnonouirechercheInternationa
Reoptimization of the Maximum Weighted Pk-Free Subgraph Problem under Vertex Insertion
No full textThe reoptimization issue studied in this paper can be described as follows: given an instance I of some problem Π, an optimal solution OPT for Π in I and an instance I′ resulting from a local perturbation of I that consists of insertions or removals of a small number of data, we wish to use OPT in order to solve Π in I′, either optimally or by guaranteeing an approximation ratio better than that guaranteed by an ex nihilo computation and with running time better than that needed for such a computation. In this setting we study the weighted version of max weighted P k -free subgraph. We then show, how the technique we use allows us to handle also bin packing.ouinonouirechercheInternationa
Symmetric Excited States for a Mean-Field Model for a Nucleon
No full textIn this paper, we consider a stationary model for a nucleon interacting with the ω and σ mesons in the atomic nucleus. The model is relativistic, and we study it in a nuclear physics nonrelativistic limit. By a shooting method, we prove the existence of infinitely many solutions with a given angular momentum. These solutions are ordered by the number of nodes of each component.nonnonouirechercheInternationa
Nouvelles techniques de gestion et leur impact sur la volatilité
No full textLa gestion alternative s'est considérablement développée ces dernières années. Cependant l'impact sur les marchés et plus précisément sur la volatilité des marchés des nouvelles techniques de gestion qui l'accompagne est méconnu. Cet article se propose d'explorer le lien entre le développement de nouvelles pratiques de gestion et l'évolution de la volatilité, dont l'étape intermédiaire est l'étude du lien entre pratiques de gestion et volume.New investment management techniques and their impact on volatility The growth of alternative investment has been considerable in recent years. However, the impact on markets or more precisely, on markets volatility, of the new induced management techniques is still not clear. In this article, we undergo such an analysis. We first link investment strategies to volume before analysing the volume-volatility relation.nonouirechercheNationa
Special Issue on Formal Models of Belief Change in Rational Agents
No full textnonouirechercheInternationa
Reliable ABC model choice via random forests
No full textMotivation: Approximate Bayesian computation (ABC) methods provide an elaborate approach to Bayesian inference on complex models, including model choice. Both theoretical arguments and simulation experiments indicate, however, that model posterior probabilities may be poorly evaluated by standard ABC techniques.Results: We propose a novel approach based on a machine learning tool named random forests (RF) to conduct selection among the highly complex models covered by ABC algorithms. We thus modify the way Bayesian model selection is both understood and operated, in that we rephrase the inferential goal as a classification problem, first predicting the model that best fits the data with RF and postponing the approximation of the posterior probability of the selected model for a second stage also relying on RF. Compared with earlier implementations of ABC model choice, the ABC RF approach offers several potential improvements: (i) it often has a larger discriminative power among the competing models, (ii) it is more robust against the number and choice of statistics summarizing the data, (iii) the computing effort is drastically reduced (with a gain in computation efficiency of at least 50) and (iv) it includes an approximation of the posterior probability of the selected model. The call to RF will undoubtedly extend the range of size of datasets and complexity of models that ABC can handle. We illustrate the power of this novel methodology by analyzing controlled experiments as well as genuine population genetics datasets.Availability and implementation: The proposed methodology is implemented in the R package abcrf available on the CRAN.Contact: [email protected] information: Supplementary data are available at Bioinformatics online.nonnonouirechercheInternationa
Equilibrium Fluctuations for a Non Gradient Energy Conserving Stochastic Model
No full textIn this paper we study the equilibrium energy fluctuation field of a one-dimensional reversible non gradient model. We prove that the limit fluctuation process is governed by a generalized Ornstein-Uhlenbeck process, whose covariances are given in terms of the diffusion coefficient.The fact that the conserved, quantity (energy) is not a linear functional of the coordinates of the system: introduces new difficulties of a geometric nature when adapting the non gradient method introduced by Varadhan.nonouirechercheInternationa