Performance characterization of black boxes with self-controlled load injection for simulation-based sizing

International audienceSizing and capacity planning are key issues that must be addressed by anyone wanting to ensure a distributed system will sustain an expected workload. Solutions typically consist in either benchmarking,or modeling and simulating the target system. However, full-scale benchmarking may be too costly and almost impossible, while the granularity of modeling is often limited by the huge complexity and the lack of information about the system. To extract a model for this kind of system, we propose a methodology that combines both solutions by first identifying a middle-grain model made of interconnected black boxes, and then to separately characterize the performance and resource consumption of these black boxes. Then, we present two important issues : saturation and stability, that are key to system capacity characterization. To experiment our methodology, we propose a component-based supporting architecture, introducing control theory issues in a general approach to autonomic computing infrastructures

Bladder segmentation in MRI images using active region growing model.

International audienceProstate segmentation in MRI may be difficult at the interface with the bladder where the contrast is poor. Coupled-models that segment simultaneously both organs with non-overlapping constraints offer a good solution. As a pre-segmentation of the structures of interest is required, we propose in this paper a fast deformable model to segment the bladder. The combination of inflation and internal forces, locally adapted according to the gray levels, allow to deform the mesh toward the boundaries while overcoming the leakage issues that can occur at weak edges. The algorithm, evaluated on 33 MRI volumes from 5 different devices, has shown good performance providing a smooth and accurate surface

Intra subject 3D/3D Kidney Registration using Local Mutual Information Maximization

International audienceOne of the goal of the Nephron-Sparing Surgery properative planning is to delineate as exactly as possible the renal carcinoma and to specify its relations to the renal arterial, venous and collecting system anatomies. The classical preoperative imaging system is the Spiral CT Urography, which gives sucessive 3D acquisitions of complementary information The integration of this information within the a patient spacific anatomical referential can be achieved by intra-patient registration techniques. A local MI maximization registration method is proposed in this paper. The kidneys are extracted from the abdomen volumes and then the registration between the extracted kidneys is implemented by maximizing the MI between them. The experimental results demonstrates that this method is effective

CNN-based real-time 2D-3D deformable registration from a single X-ray projection

Purpose: The purpose of this paper is to present a method for real-time 2D-3D non-rigid registration using a single fluoroscopic image. Such a method can find applications in surgery, interventional radiology and radiotherapy. By estimating a three-dimensional displacement field from a 2D X-ray image, anatomical structures segmented in the preoperative scan can be projected onto the 2D image, thus providing a mixed reality view. Methods: A dataset composed of displacement fields and 2D projections of the anatomy is generated from the preoperative scan. From this dataset, a neural network is trained to recover the unknown 3D displacement field from a single projection image. Results: Our method is validated on lung 4D CT data at different stages of the lung deformation. The training is performed on a 3D CT using random (non domain-specific) diffeomorphic deformations, to which perturbations mimicking the pose uncertainty are added. The model achieves a mean TRE over a series of landmarks ranging from 2.3 to 5.5 mm depending on the amplitude of deformation. Conclusion: In this paper, a CNN-based method for real-time 2D-3D non-rigid registration is presented. This method is able to cope with pose estimation uncertainties, making it applicable to actual clinical scenarios, such as lung surgery, where the C-arm pose is planned before the intervention

Experimental Methodology for the Evaluation of the 3D Visualization of Quantitative Information: a Case Study Concerning SEEG Information

The visual analysis of Stereoeletroencephalographic (SEEG) signals in their anatomical context is aimed at understanding the spatio-temporal dynamics of epileptic processes. The magnitude of these signals may be encoded by graphical glyphs, having a direct impact on the perception of the values. This problem has motivated an evaluation of the quantitative visualization of these signals, specifically with regard to the influence of the coding scheme of the glyphs on the understanding and analysis of the signals. This work describes an experiment conducted with human observers in order to evaluate three different coding schemes used to visualize the magnitude of SEEG signals in their 3D anatomical context. Before the experiment we had no clue to which of these schemes would provide better performance to the human observers, while the literature offered theories supporting different answers. Through our experiment we intended to find out if any of these coding schemes allows better performance in two aspects: accuracy and speed. A protocol has been developed to measure these aspects. The results presented in this work were obtained from 40 human observers. Comparison between the three coding schemes was first performed through an Exploratory Data Analysis (EDA). Statistical significance of this comparison was then established using nonparametric methods. Influence of some other factors on the observers’ performance was also investigated

Model-Based Performance Anticipation in Multi-tier Autonomic Systems: Methodology and Experiments

http://www.thinkmind.org/download.php?articleid=netser_v3_n34_2010_3International audienceThis paper advocates for the introduction of perfor- mance awareness in autonomic systems. Our goal is to introduce performance prediction of a possible target configuration when a self-* feature is planning a system reconfiguration. We propose a global and partially automated process based on queues and queuing networks modelling. This process includes decomposing a distributed application into black boxes, identifying the queue model for each black box and assembling these models into a queuing network according to the candidate target configuration. Finally, performance prediction is performed either through simulation or analysis. This paper sketches the global process and focuses on the black box model identification step. This step is automated thanks to a load testing platform enhanced with a workload control loop. Model identification is based on statistical tests. The identified models are then used in performance prediction of autonomic system configurations. This paper describes the whole process through a practical experiment with a multi-tier application

Model-Based Performance Anticipation in Multi-tier Autonomic Systems: Methodology and Experiments

http://www.thinkmind.org/download.php?articleid=netser_v3_n34_2010_3International audienceThis paper advocates for the introduction of perfor- mance awareness in autonomic systems. Our goal is to introduce performance prediction of a possible target configuration when a self-* feature is planning a system reconfiguration. We propose a global and partially automated process based on queues and queuing networks modelling. This process includes decomposing a distributed application into black boxes, identifying the queue model for each black box and assembling these models into a queuing network according to the candidate target configuration. Finally, performance prediction is performed either through simulation or analysis. This paper sketches the global process and focuses on the black box model identification step. This step is automated thanks to a load testing platform enhanced with a workload control loop. Model identification is based on statistical tests. The identified models are then used in performance prediction of autonomic system configurations. This paper describes the whole process through a practical experiment with a multi-tier application