Isotropic-Resolution Tomographic Diffractive Microscopy

International audienceMicroscopy techniques based on recording of the optical field diffracted by the specimen, in amplitude and phase, like Digital Holographic Microscopy (DHM) have been a growing research topic in recent years. Tomographic acquisitions are possible if one is able to record information, while controlling variations of the specimen illumination. Classical approaches consider either illumination variation, simple to implement, but suffering fro the classical "missing cone" problem, or sample rotation, delivering images with quasi-isotropic, but lower resolution. We have developed an original-, combined tomographic diffractive microscope setup, making use of specimen rotation as well as illumination rotation, which is able to deliver images with an almost isotropic resolution better than 200 nm

Tomographic diffractive microscopy: towards highresolution 3-D real-time data acquisition, image reconstruction and display of unlabeled samples

Tomographic diffractive microscopy allows for imaging unlabeled specimens, with a better resolution than conventional microscopes, giving access to the index of refraction distribution within the specimen, and possibly at high speed. Principles of image formation and reconstruction are presented, and progresses towards realtime, three-dimensional acquisition, image reconstruction and final display, are discussed

Automatic Statistical Shape Model Building from A Priori

We propose a method based on a priori knowledge provided by anatomical atlases to build almost automatically a Point Distribution Model of internal brain structures. A trainingset of 3D shapes is first constructed by registering an anatomical atlas over a MRI database. Then, the set of shape instances is automatically landmarked in a Point Distribution Model optimization framework: according to the recent method developed by Davies et al. [1], a Minimum Description Length principle-based objective function drives the optimization process to the "best" annotation for input training set. Preliminary results of models built for several anatomical structures are encouraging

Automatic atlas-based building of point distribution model for segmentation of anatomical structures from brain MRI

We propose a method based on a priori knowledge provided by anatomical atlases to build - almost - automatically a Point Distribution Model (PDM) of internal brain structures. A 3D training set of shapes is constructed by registering chosen atlas over an MRI database, which is then landmarked using amethod recently developed by Davies et al. This PDM global optimization process is driven by a Minimum Description Length (MDL) principle-based objective function. Preliminary results of built PDMs are encouraging. Future work might build Active Appearance Models (AAM) from computed PDMs to set up a segmentation method

Construction automatique de modèles de formes a partir d'informations a priori

National audienceNous proposons une méthode exploitant des connaissances a priori modélisées sous forme d'un atlas anatomique pour construire automatiquement un modèle de forme (PDM) de structures cérébrales en IRM 3D. Un ensemble d'apprentissage de formes 3D est d'abord construit par recalage de l'atlas sur un ensemble d'IRMs de patients. Ensuite, elles sont automatiquement annotées (disposition de points caractéristiques) via une méthode récemment développée par Davies et al. assurant une qualité optimale du modèle dérivé recourant à la théorie de "Minimum Description Length". Finalement, l'usage de ce modèle dans le cadre d'une délinéation automatique des structures cérébrales à partir d'IRMs 3D est envisagé

Automatic Statistical Shape Model Building from A Priori Information

International audienceWe propose a method based on a priori knowledge provided by anatomical atlases to build almost automatically a Point Distribution Model of internal brain structures. A training set of 3D shapes is rst constructed by registering an anatomical atlas over a MRI database. Then, the set shape instances are automatically landmarked in a Point Distribution Model optimization framework: according to the recent method developed by Davies et al. [1], a Minimum Description Length principle-based objective function drives the optimization process to the \best" annotation for input training set. Preliminary results of models built for several anatomical structures are encouraging

A “Numerical Evo-Devo” Synthesis for the Identification of Pattern-Forming Factors

Animals display extensive diversity in motifs adorning their coat, yet these patterns have reproducible orientation and periodicity within species or groups. Morphological variation has been traditionally used to dissect the genetic basis of evolutionary change, while pattern conservation and stability in both mathematical and organismal models has served to identify core developmental events. Two patterning theories, namely instruction and self-organisation, emerged from this work. Combined, they provide an appealing explanation for how natural patterns form and evolve, but in vivo factors underlying these mechanisms remain elusive. By bridging developmental biology and mathematics, novel frameworks recently allowed breakthroughs in our understanding of pattern establishment, unveiling how patterning strategies combine in space and time, or the importance of tissue morphogenesis in generating positional information. Adding results from surveys of natural variation to these empirical-modelling dialogues improves model inference, analysis, and in vivo testing. In this evo-devo-numerical synthesis, mathematical models have to reproduce not only given stable patterns but also the dynamics of their emergence, and the extent of inter-species variation in these dynamics through minimal parameter change. This integrative approach can help in disentangling molecular, cellular and mechanical interaction during pattern establishment

Statistical shape model-based segmentation of brain MRI images

International audienceWe propose a segmentation method that automatically delineates structures contours from 3D brain MRI images using a statistical shape model. We automatically build this 3D Point Distribution Model (PDM) in applying a Minimum Description Length (MDL) annotation to a training set of shapes, obtained by registration of a 3D anatomical atlas over a set of patients brain MRIs. Delineation of any structure from a new MRI image is first initialized by such registration. Then, delineation is achieved in iterating two consecutive steps until the 3D contour reaches idempotence. The first step consists in applying an intensity model to the latest shape position so as to formulate a closer guess: our model requires far less priors than standard model in aiming at direct interpretation rather than compliance to learned contexts. The second step consists in enforcing shape constraints onto previous guess so as to remove all bias induced by artifacts or low contrast on current MRI. For this, we infer the closest shape instance from the PDM shape space using a new estimation method which accuracy is significantly improved by a huge increase in the model resolution and by a depth-search in the parameter space. The delineation results we obtained are very encouraging and show the interest of the proposed framework

Segmentation of anatomical structures from 3D brain MRI using automatically-built statistical shape models

International audienceWe propose a twofold method that rst automatically builds a statistical shape model of anatomical 3D brain structures of interest, then uses this model for delineating structure contours onto any patient MRI. First of all, an estimated training set of shapes is infered by registration of a 3D anatomical atlas over a set of brain MRIs, then automatically landmarked using the "Minimum Description Length" based method developed by Davies et al.[4]. A 3D "Point Distribution Model" is then established and used to constrain the delineation process. It is lead by a novel intensity model speci cally developed to overcome the estimated nature of our training set in exploiting only local intensities

SEGMENTATION OF ANATOMICAL STRUCTURES FROM 3D BRAIN MRI USING AUTOMATICALLY-BUILT STATISTICAL SHAPE MODELS

We propose a twofold method that first automatically builds a statistical shape model of anatomical 3D brain structures of interest, then uses this model for delineating structure contours onto any patient MRI. First of all, an estimated training set of shapes is infered by registration of a 3D anatomical atlas over a set of brain MRIs, then automatically landmarked using the “Minimum Description Length ” based method developed by Davies et al.[4]. A 3D “Point Distribution Model ” is then established and used to constrain the delineation process. It is lead by a novel intensity model specifically developed to overcome the estimated nature of our training set in exploiting only local intensities. 1