Estimation des paramètres du modèle de croissance et d'architecture végétale AMAPpara. Application au cotonnier taillé

La simulation informatique de la croissance des plantes a été abordée selon d'une part, une approche physiologique globale et une approche botanique architecturale d'autre part. Seuls les modèles informatiques à base écophysiologique les plus récents tentent de synthétiser ces deux approches. Le modèle de croissance AMAPpara développé par le Cirad vise à intégrer les données morphologiques et architecturales essentielles de l'arbre et l'action des facteurs écophysiologiques. Une validation expérimentale du modèle a été entreprise à partir du cotonnier comme plante modèle. Ce travail présente une méthode informatique pour déterminer les valeurs des paramètres du modèle AMAPpara à partir des données expérimentales sur cotonnie

Filtrage et segmentation d'images tridimensionnelles (application à la détection et à la caractérisation des structures anatomiques et pathologiques du foie)

MONTPELLIER-BU Sciences (341722106) / SudocSudocFranceF

Automatic detection of liver tumors

International audienceTumor detection in CT liver images is a challenging task. The nature of tumor has a direct effect on the number of voxels being contaminated, as well as on the changes in the observed CT scan. In order to deal with this challenge, in this paper we propose the use of advanced non-linear machine learning techniques to determine the optimal features, as well as the hyperplane that use these features to separate tumoral voxels from voxels corresponding to healthy tissues. Very promising classification results using an important volume of clinically annotated data (86% sensitivity, 82% specificity) demonstrate the potentials of our approach

Graph-based Detection, Segmentation & Characterization of Brain Tumors

International audienceIn this paper we propose a novel approach for detection, segmentation and characterization of brain tumors. Our method exploits prior knowledge in the form of a sparse graph representing the expected spatial positions of tumor classes. Such information is coupled with image based classification techniques along with spatial smoothness constraints towards producing a reliable detection map within a unified graphical model formulation. Towards optimal use of prior knowledge, a two layer interconnected graph is considered with one layer corresponding to the low-grade glioma type (characterization) and the second layer to voxel-based decisions of tumor presence. Efficient linear programming both in terms of performance as well as in terms of computational load is considered to recover the lowest potential of the objective function. The outcome of the method refers to both tumor segmentation as well as their characterization. Promising results on substantial data sets demonstrate the extreme potentials of our method

Graph Based Spatial Position Mapping of Low-grade Gliomas

International audienceLow-grade gliomas (WHO grade II) are diffusively infiltra- tive brain tumors arising from glial cells. Spatial classification that is usually based on cerebral lobes lacks accuracy and is far from being able to provide some pattern or statistical interpretation of their appearance. In this paper, we propose a novel approach to understand and infer position of low-grade gliomas using a graphical model. The problem is formulated as a graph topology optimization problem. Graph nodes correspond to extracted tumors and graph connections to the spatial and content dependencies among them. The task of spatial position mapping is then expressed as an unsupervised clustering problem, where cluster centers correspond to centers with position appearance prior, and cluster samples to nodes with strong statistical dependencies on their position with respect to the cluster center. Promising results using leave-one-out cross-validation outperform conventional dimensionality reduction methods and seem to coincide with conclusions drawn in physiological studies regarding the expected tumor spatial distributions and interactions