Jean-Loïc Rose

Influence of age and sex on aortic distensibility assessed by MRI in healthy subjects

Magnetic resonance imaging (MRI) is particularly well adapted to the evaluation of aortic distensibility. The calculation of this parameter, based on the change in vessel cross-sectional area per unit change in blood pressure, requires precise delineation of the aortic wall on a series of cine-MR images. Firstly, the study consisted in validating a new automatic method to assess aortic elasticity. Secondly, aortic distensibility was studied for the ascending and descending thoracic aortas in 26 healthy subjects. Two homogeneous groups were available to evaluate the influence of sex and age (with an age limit value of 35 years). The automatic postprocessing method proved to be robust and reliable enough to automatically determine aortic distensibility, even on artefacted images. In the 26 healthy volunteers, a marked decrease in distensibility appears with age, although this decrease is only significant for the ascending aorta (8.97+/-2.69 10(-3) mmHg(-1) vs. 5.97+/-2.02 10(-3) mmHg(-1)). Women have a higher aortic distensibility than men but only significantly at the level of the descending aorta (7.20+/-1.61 10(-3) mmHg(-1) vs. 5.05+/-2.40 10(-3) mmHg(-1)). Through our automatic contouring method, the aortic distensibility from routine cine-MRI has been studied on a healthy subject population providing reference values of aortic stiffness. The aortic distensibility calculation shows that age and sex are causes of aortic stiffness variations in healthy subjects.

Vesselness-guided variational segmentation of cellular networks from 3D micro-CT

Advances in imaging techniques lead to nondestructive 3D visualization of biological tissue at a sub-cellular scale. As a consequence, new demands emerge to segment complex structures. For instance, synchrotron radiation micro-CT, makes it possible to image the lacunar-canalicular porosity in bone tissue. This structure contains a dense network of slender channels interconnecting the cells. Their size (~300-600 nanometers in diameter) is at the limit of the acquisition system resolution (280 nm) making their detection difficult. In this work is proposed a variational region growing segmentation method adapted for cellular networks. To control the evolution of the segmentation through tubular structures a vesselness map is introduced in the expression of the functional to minimize. The method is tested on synthetic images and applied to experimental data.

Shape Prior Integrated in an Automated 3D Region Growing Method

We propose a new automated region growing method integrating shape prior (RGISP). The aim of this work is to improve region growing segmentation by taking into account a reference model. Our algorithm is assessed on a synthesized image and compared with two other methods in order to point up the contribution of shape prior. It was also applied to segment in-vivo mu-CT images of mouse kidneys in the framework of small animal imaging. RGISP gives promising results and appears to be well adapted to satisfy small animal imaging constraints.

Region tracking with narrow perception of background

We address the problem of object tracking within image sequences through region-based energy minimization. A common underlying assumption in region tracking is that color statistics can be confidently estimated in a global manner over object and background regions. This can be a drawback for tracking in real scenes with cluttered backgrounds, where statistical color data is highly scattered, preventing the estimation of reliable color statistics for object/background discrimination. To overcome this limitation, we propose an approach based on a narrow perception of background, which concentrates on the vicinity of tracked objects and thus extract more consistent statistical data for region separation. The benefits of our approach are demonstrated using two different statistical color models.

Shape prior in Variational Region Growing

In this paper, we propose two solutions to integrate shape prior in a segmentation process based on region growing. Our special region growing algorithm relies upon a variational framework which allows to easily take into account shape prior in the segmentation process. Region growing is described as an optimization process that aims to minimize some special energy combining intensity function and shape information. Two kinds of energy are proposed depending on the existence of a reference model or the possibility to assess some shape features at voxel level. We applied positively these two approaches in the context of life imaging in order to segment mice kidneys from small animal CT-images and lacuno-canicular network from experimental high resolution Synchrotron Radiation X-Ray Computed Tomography (SRμCT) images.

Region Growing: When Simplicity Meets Theory – Region Growing Revisited in Feature Space and Variational Framework

Region growing is one of the most intuitive techniques for image segmentation. Starting from one or more seeds, it seeks to extract meaningful objects by iteratively aggregating surrounding pixels. Starting from this simple description, we propose to show how region growing technique can be elevated to the same rank as more recent and sophisticated methods. Two formalisms are presented to describe the process. The first one derived from non-parametric estimation relies upon feature space and kernel functions. The second one is issued from a variational framework, describing the region evolution as a process which minimizes an energy functional. It thus proves the convergence of the process and takes advantage of the huge amount of work already done on energy functionals. In the last part, we illustrate the interest of both formalisms in the context of life imaging. Three segmentation applications are considered using various modalities such as whole body PET imaging, small animal μCT imaging and experimental Synchrotron Radiation μCT imaging. We will thus demonstrate that region growing has reached this last decade a maturation that offers many perspectives of applications to the method.

Multitarget region tracking based on short-sight modelling of background and color distribution temporal variation.

We address the problem of multitarget region tracking within image sequences. Following recent work on joint segmentation and tracking as well as non-parametric modeling of color statistics, we develop an energy-minimization based approach using color histograms measures. As in a few other existing approaches, a single color probability distribution per object and background is handled. In this context, global histograms may be problematic for tracking in real scenes with cluttered backgrounds, where statistical color data is highly scattered, preventing the estimation of reliable color statistics for object/background discrimination. To overcome this limitation, we introduce a short-sight perception modeling of background, which concentrates on the vicinity of tracked objects and thus extract more consistent statistical data for accurate separation between objects and background. To account for temporal consistency, our energy is also endowed with a novel data term explicitly based on temporal variation of color distribution within objects and local background regions.