Active contour segmentation with a parametric shape prior: Link with the shape gradient

International audienceActive contours are adapted to image segmentation by energy minimization. The energies often exhibit local minima, requiring regularization. Such an a priori can be expressed as a shape prior and used in two main ways: (1) a shape prior energy is combined with the segmentation energy into a trade-off between prior compliance and accuracy or (2) the segmentation energy is minimized in the space defined by a parametric shape prior. Methods (1) require the tuning of a data-dependent balance parameter and methods (1) and (2) are often dedicated to a specific prior or contour representation, with the prior and segmentation aspects often meshed together, increasing complexity. A general framework for category (2) is proposed: it is independent of the prior and contour representations and it separates the prior and segmentation aspects. It relies on the relationship shown here between the shape gradient, the prior-induced admissible contour transformations, and the segmentation energy minimization

Is the Vascular Network Discriminant Enough to Classify Renal Cell Carcinoma?

International audienceThe renal cell carcinoma (RCC) is the most frequent type of kidney cancer (between 90% and 95%). Twelve subtypes of RCC can be distinguished, among which the clear cell carcinoma (ccRCC) and the papillary carcinoma (pRCC) are the two most common ones (75% and 10% of the cases, respectively). After resection (i.e., surgical removal), the tumor is prepared for histological examination (fixation, slicing, staining, observation with a microscope). Along with protein expression and genetic tests, the histological study allows to classify the tumor and define its grade in order to make a prognosis and to take decisions for a potential additional chemotherapy treatment. Digital histology is a recent domain, since routinely, histological slices are studied directly under the microscope. The pioneer works deal with the automatic analysis of cells. However, a crucial factor for RCC classification is the tumoral architecture relying on the structure of the vascular network. For example, coarsely speaking, ccRCC is characterized by a ``fishnet'' structure while the pRCC has a tree-like structure. To our knowledge, no computerized analysis of the vascular network has been proposed yet. In this context, we developed a complete pipeline to extract the vascular network of a given histological slice and compute features of the underlying graph structure. Then, we studied the potential of such a feature-based approach in classifying a tumor into ccRCC or pRCC. Preliminary results on patient data are encouraging

A Recursive Approach For Multiclass Support Vector Machine: Application to Automatic Classification of Endomicroscopic Videos

International audienceThe two classical steps of image or video classification are: image signature extraction and assignment of a class based on this image signature. The class assignment rule can be learned from a training set composed of sample images manually classified by experts. This is known as supervised statistical learning. The well-known Support Vector Machine (SVM) learning method was designed for two classes. Among the proposed extensions to multiclass (three classes or more), the one-versus-one and one-versus-all approaches are the most popular ones. This work presents an alternative approach to extending the original SVM method to multiclass. A tree of SVMs is built using a recursive learning strategy, achieving a linear worst-case complexity in terms of number of classes for classification. During learning, at each node of the tree, a bi-partition of the current set of classes is determined to optimally separate the current classification problem into two sub-problems. Rather than relying on an exhaustive search among all possible subsets of classes, the partition is obtained by building a graph representing the current problem and looking for a minimum cut of it. The proposed method is applied to classification of endomicroscopic videos and compared to classical multiclass approaches

A study on tensor and matrix models for super-resolution fluorescence microscopy

International audienceSuper-resolution techniques for fluorescence microscopy areinvaluable tools for studying phenomena that take place atsub-cellular scales, thanks to their capability of overcominglight diffraction. Yet, achieving sufficient temporal resolutionfor imaging live-cell processes remains a challenging prob-lem. Exploiting the temporal fluctuations (blinking) of fluo-rophores is a promising approach that allows employing stan-dard equipment and harmless excitation levels. In this work,we study a novel constrained tensor modeling approach thattakes this temporal diversity into account to estimate the spa-tial distribution of fluorophores and their overall intensities.We compare this approach with an also novel matrix-basedformulation which promotes structured sparsity via a continu-ous approximation of the cardinality function, as well as withother state-of-the-art methods

SPADE: A Small Particle Detection Method Using A Dictionary Of Shapes Within The Marked Point Process Framework

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An image based high throughput screen to identify regulators of Imp containing RNP granules

International audienceIn vivo, RNAs and proteins are frequently packaged into diverse dynamic macromolecular structures named mRNP granules. These assemblies form upon phase separation of individual RNA and protein components, a process involving the establishment of multivalent weak interactions and their regulations via post-translational modifications. Defects in their properties have been associated with several human pathologies. However, our knowledge of these dynamic structures relies essentially on the study of P bodies and stress granules. We are interested in the highly conserved RNA binding protein Imp whose mammalian counterpart's overexpression correlates with poor prognosis in several cancers. In vivo, Imp is present in cytoplasmic RNP granules, distinct from P-bodies and visible both in neuronal cell bodies and axons. They are also detected in Drosophila S2R + cultured cells. Taking advantage of this cellular model, we have undertaken a genome-wide RNAi-based visual screen to identify factors that regulate the properties of Imp-containing granules. This implies combining high throughput microscopy with the development of a computational pipeline for automatic image analysis. This pipeline first segments and discriminates healthy from dead nuclei, storing this information in an interactive SQLite database that enables experimental quality control. Then, GFP-Imp granules are detected using the SPADE algorithm in the cytoplasm of healthy cells. Data from the pilot screen we have performed to validate the experimental design and develop our pipeline for data mining are presented

Segmentation par contours actifs en imagerie médicale dynamique : application en cardiologie nucléaire

In emission imaging, nuclear medicine provides functional information about the organ of interest. In transmission imaging, it provides anatomical information whose goal may be the correction of physical phenomena that corrupt emission images. With both emission and transmission images, it is useful to know how to extract, either automatically or semiautomatically, the organs of interest and the body outline in the case of a large field of view. This is the aim of segmentation. We developed two active contour segmentation methods. They were implemented using level sets. The key point is the evolution velocity definition. First, we were interested in static transmission imaging of the thorax. The evolution velocity was heuristically defined and depended only on the acquired projections. The segmented transmission map was computed w/o reconstruction and could be advantageously used for attenuation correction. Then, we studied the segmentation of cardiac gated sequences. The developed space-time segmentation method results from the minimization of a variational criterion which takes into account the whole sequence. The computed segmentation could be used for calculating physiological parameters. As an illustration, we computed the ejection fraction. Finally, we exploited some level set properties to develop a non-rigid, non-parametric, and geometric registration method. We applied it for kinetic compensation of cardiac gated sequences. The registered images were then added together providing an image with noise characteristics similar to a cardiac static image but w/o motion-induced blurring.En imagerie d'émission, la médecine nucléaire fournit une information fonctionnelle sur l'organe étudié. En imagerie de transmission, elle fournit une information anatomique, destinée par exemple à corriger certains facteurs de dégradation des images d'émission. Qu'il s'agisse d'une image d'émission ou de transmission, il est utile de savoir extraire de façon automatique ou semi-automatique les éléments pertinents : le ou les organes d'intérêt et le pourtour du patient lorsque le champ d'acquisition est large. Voilà le but des méthodes de segmentation. Nous avons développé deux méthodes de segmentation par contours actifs, le point crucial étant la définition de leur vitesse d'évolution. Elles ont été mises en œuvre par les ensembles de niveaux. En premier lieu, nous nous sommes intéressés à l'imagerie statique de transmission de la région thoracique. La vitesse d'évolution, définie heuristiquement, fait directement intervenir les projections acquises. La carte de transmission segmentée, obtenue ainsi sans reconstruction, doit servir à améliorer la correction de l'atténuation photonique subie par les images cardiaques d'émission. Puis nous avons étudié la segmentation des séquences cardiaques -- d'émission -- synchronisées par électrocardiogramme. La méthode de segmentation spatio-temporelle développée résulte de la minimisation d'un critère variationnel exploitant d'un bloc l'ensemble de la séquence. La segmentation obtenue doit servir au calcul de paramètres physiologiques. Nous l'avons illustré en calculant la fraction d'éjection. Pour terminer, nous avons exploité les propriétés des ensembles de niveaux afin de développer une méthode géométrique de recalage, non rigide et non paramétrique. Nous l'avons appliquée à la compensation cinétique des images des séquences cardiaques synchronisées. Les images recalées ont alors été ajoutées de sorte à produire une image dont le niveau de bruit est comparable à celui d'une image cardiaque statique sans toutefois souffrir de flou cinétique

Mesures de similarité statistiques et estimateurs par k plus proches voisins : une association pour gérer des descripteurs de haute dimension en traitement d'images et de vidéos

Statistical similarity measures and k nearest neighbor estimators teamed up to handle high-dimensional descriptors in image and video processingMesures de similarité statistiques et estimateurs par k plus proches voisins : une association pour gérer des descripteurs de haute dimension en traitement d'images et de vidéo