Simultaneous segmentation of the left and right heart ventricles in 3D cine MR images of small animals

New high resolution image techniques allow to capture the anatomy and movement of the heart of small animals. The availability of these in vivo images can be very useful for medical research, however the amount of generated data for large animal studies makes manual analysis a very tedious task. To cope with the problem of automatic analysis of these images, we propose the use of the Deformable Elastic Template method to perform automatic segmentation of the ventricles. To adapt the method to the specificities of high-resolution MRI, several improvements are presented, including an image-context dependent scheme for more robust segmentation. Qualitative results show that our method is able to correctly retrieve the heart’s contours in 3D. 1

Model computation and matching with the neuractive pyramid

This paper introduces the neuractive pyramids to model objects under elastic deformations . One pyramid is built on each frame of the image sequence . Each level of the pyramid is a regular graph that is recursively built on a low-pass version of the original picture . Cells of this graph deform to model the local information of the picture . Deformations are obtained by the minimization of an energy function computed both on the gradient of the picture and the graph structure . Each cell contains a vector of statistical moments computed on its domain and its neighborood . Matching pyramids defines a vector Field of local elastic transformations . The matching operator is based on a self-organizing map, introduced by Kohonen . Softness and multiresolution aspects of the pyramids allow accurate and robust results . An application to the matching of 2D cardiac MRI scans shows the interest of the method for deformable objects .Cet article présente les pyramides neuractives pour la modélisation des objets subissant des déformations élastiques. Une pyramide est construite pour chaque image de la séquence. Chaque niveau de la pyramide est un graphe régulier construit récursivement sur l'image de départ convoluée par un filtre passe-bas. Les cellules de ce graphe s'adaptent au contenu local des images. L'adaptation du graphe est obtenue en minimisant une fonction énergétique basée sur le gradient de l'image et la déformation des cellules. Chaque cellule reçoit un vecteur de moments statistiques calculé sur sa zone d'intérêt et celles de ses voisines. La mise en correspondance des pyramides permet d'accéder au champ de vecteurs des transformations élastiques locales. L'algorithme de mise en correspondance est fondé sur une approche neuronale auto-organisatrice. La souplesse et l'aspect multirésolution des structures permettent d'obtenir des résultats robustes et précis. L'application à des images cardiaques obtenues par résonance magnétique (IRM) 2D montre l'intérêt de la méthode pour la modélisation d'objets complexes

Electrical Characterization of Submicrometer Silicon Devices by Cross-Sectional Contact Mode Atomic Force Microscopy

Two contact mode atomic force microscopic (AFM) techniques under ambient conditions are presented for the electrical evaluation of cross sectioned silicon devices. In the first technique, a conductive AFM tip is used as a voltage probe to determine the local potential distribution on the cross section of a silicon device under operation. The electrical potential is measured simultaneously with the surface topography with nanometer resolution and mV accuracy, offering an easy way of correlating topographic and electrical features. A second method, nanometer spreading resistance profiling (nano-SRP), performs localized spreading resistance measurements to determine the spatial distribution of charge carriers in silicon structures. The conversion of the resistance profiles into charge carrier profiles as well as the applied correction factors are discussed in more detail. Both methods are used to map electrical characteristics of state-of-the-art silicon structures

A dynamic 3-D cardiac surface model from MR images

Cardiac 3D + time segmentation and motion estimation are recognized as difficult prerequisite tasks for any quan-titative analysis of cardiac images. Some recent algorithms aim to consider a temporal constraint to increase the ac-curacy of results. To improve the temporal consistency, prior knowledge about cardiac dynamics can be used. In this paper, we propose to build a new Statistical Dynamic Model (SDM) of the heart by learning through a popula-tion of healthy individuals. This SDM is composed by a set of semi-landmarks which describe the heart surfaces. For each of them, a mean trajectory and variability around it are derived. The SDM provides a reasonable constraint for a temporally regularized segmentation and motion track-ing algorithm. 1

Myocardial Extracellular Volume Estimation by CMR Predicts Functional Recovery Following Acute MI

Objectives: In the setting of reperfused acute myocardial infarction (AMI), the authors sought to compare prediction of contractile recovery by infarct extracellular volume (ECV), as measured by T1-mapping cardiac magnetic resonance (CMR), with late gadolinium enhancement (LGE) transmural extent. Background: The transmural extent of myocardial infarction as assessed by LGE CMR is a strong predictor of functional recovery, but accuracy of the technique may be reduced in AMI. ECV mapping by CMR can provide a continuous measure associated with the severity of tissue damage within infarcted myocardium. Methods: Thirty-nine patients underwent acute (day 2) and convalescent (3 months) CMR scans following AMI. Cine imaging, tissue tagging, T2-weighted imaging, modified Look-Locker inversion T1 mapping natively and 15 min post–gadolinium-contrast administration, and LGE imaging were performed. The ability of acute infarct ECV and acute transmural extent of LGE to predict convalescent wall motion, ejection fraction (EF), and strain were compared per-segment and per-patient. Results: Per-segment, acute ECV and LGE transmural extent were associated with convalescent wall motion score (p < 0.01; p < 0.01, respectively). ECV had higher accuracy than LGE extent to predict improved wall motion (area under receiver-operating characteristics curve 0.77 vs. 0.66; p = 0.02). Infarct ECV ≤0.5 had sensitivity 81% and specificity 65% for prediction of improvement in segmental function; LGE transmural extent ≤0.5 had sensitivity 61% and specificity 71%. Per-patient, ECV and LGE correlated with convalescent wall motion score (r = 0.45; p < 0.01; r = 0.41; p = 0.02, respectively) and convalescent EF (p < 0.01; p = 0.04). ECV and LGE extent were not significantly correlated (r = 0.34; p = 0.07). In multivariable linear regression analysis, acute infarct ECV was independently associated with convalescent infarct strain and EF (p = 0.03; p = 0.04), whereas LGE was not (p = 0.29; p = 0.24). Conclusions: Acute infarct ECV in reperfused AMI can complement LGE assessment as an additional predictor of regional and global LV functional recovery that is independent of transmural extent of infarction

Wetting of a symmetrical binary fluid mixture on a wall

We study the wetting behaviour of a symmetrical binary fluid below the demixing temperature at a non-selective attractive wall. Although it demixes in the bulk, a sufficiently thin liquid film remains mixed. On approaching liquid/vapour coexistence, however, the thickness of the liquid film increases and it may demix and then wet the substrate. We show that the wetting properties are determined by an interplay of the two length scales related to the density and the composition fluctuations. The problem is analysed within the framework of a generic two component Ginzburg-Landau functional (appropriate for systems with short-ranged interactions). This functional is minimized both numerically and analytically within a piecewise parabolic potential approximation. A number of novel surface transitions are found, including first order demixing and prewetting, continuous demixing, a tricritical point connecting the two regimes, or a critical end point beyond which the prewetting line separates a strongly and a weakly demixed film. Our results are supported by detailed Monte Carlo simulations of a symmetrical binary Lennard-Jones fluid at an attractive wall.Comment: submitted to Phys. Rev.

Multi-modality image simulation with the Virtual Imaging Platform: Illustration on cardiac echography and MRI

International audienceMedical image simulation is useful for biological modeling, image analysis, and designing new imaging devices but it is not widely available due to the complexity of simulators, the scarcity of object models, and the heaviness of the associated computations. This paper presents the Virtual Imaging Platform, an openly-accessible web platform for multi-modality image simulation. The integration of simulators and models is described and exemplified on simulated cardiac MRIs and ultrasonic images

A mutual reference shape based on information theory

International audienceIn this paper, we propose to consider the estimation of a refer-ence shape from a set of different segmentation results using both active contours and information theory. The reference shape is defined as the minimum of a criterion that benefits from both the mutual information and the joint entropy of the input segmentations and called a mutual shape. This energy criterion is here justified using similarities between informa-tion theory quantities and area measures, and presented in a continuous variational framework. This framework brings out some interesting evaluation measures such as the speci-ficity and sensitivity. In order to solve this shape optimization problem, shape derivatives are computed for each term of the criterion and interpreted as an evolution equation of an active contour. Some synthetical examples allow us to cast the light on the difference between our mutual shape and an average shape. Our framework has been considered for the estimation of a mutual shape for the evaluation of cardiac segmentation methods in MRI