Biopsym : a learning environment for transrectal ultrasound guided prostate biopsies

This paper describes a learning environment for image-guided prostate biopsies in cancer diagnosis; it is based on an ultrasound probe simulator virtually exploring real datasets obtained from patients. The aim is to make the training of young physicians easier and faster with a tool that combines lectures, biopsy simulations and recommended exercises to master this medical gesture. It will particularly help acquiring the three-dimensional representation of the prostate needed for practicing biopsy sequences. The simulator uses a haptic feedback to compute the position of the virtual probe from three-dimensional (3D) ultrasound recorded data. This paper presents the current version of this learning environment

Discrete Visual Perception

International audienceComputational vision and biomedical image have made tremendous progress of the past decade. This is mostly due the development of efficient learning and inference algorithms which allow better, faster and richer modeling of visual perception tasks. Graph-based representations are among the most prominent tools to address such perception through the casting of perception as a graph optimization problem. In this paper, we briefly introduce the interest of such representations, discuss their strength and limitations and present their application to address a variety of problems in computer vision and biomedical image analysis

Poisson-Gaussian noise parameter estimation in fluorescence microscopy imaging

International audienceIn this paper, we present a new fully automatic approach for noise parameter estimation in the context of fluorescence imaging systems. In particular, we address the problem of Poisson-Gaussian noise modeling in the nonstationary case. In microscopy practice, the nonstationarity is due to the photobleaching effect. The proposed method consists of an adequate moment based initialization followed by Expectation-Maximization iterations. This approach is shown to provide reliable estimates of the mean and the variance of the Gaussian noise and of the scale parameter of Poisson noise, as well as of the photobleaching rates. The algorithm performance is demonstrated on both synthetic and real fluorescence microscopy image sequences

Prior-based Coregistration and Cosegmentation

We propose a modular and scalable framework for dense coregistration and cosegmentation with two key characteristics: first, we substitute ground truth data with the semantic map output of a classifier; second, we combine this output with population deformable registration to improve both alignment and segmentation. Our approach deforms all volumes towards consensus, taking into account image similarities and label consistency. Our pipeline can incorporate any classifier and similarity metric. Results on two datasets, containing annotations of challenging brain structures, demonstrate the potential of our method.Comment: The first two authors contributed equall

Detection of Multiple Pathways in the Spinal Cord White Matter Using Q-Ball Imaging

International audienceHigh angular resolution MRI such as q-ball imaging (QBI) allows to recover complex white matter architecture. We applied this technique to an ex vivo spinal cord of one cat using a 3T scanner, 100 directions and b-values varying from 1000 to 3000 s/mm2. As a result, QBI can retrieve crossing fibre information, where the diffusion tensor imaging approach is constrained to a single diffusion direction. To our knowledge, this is the first study demonstrating the benefits of QBI in observing longitudinal, commissural and dorso-ventral fibres in the spinal cord. It is a first step towards in vivo characterization of the healthy and injured spinal cord using high angular resolution diffusion imaging (HARDI) and QBI

Instance nationale et multi-communauté de DIRAC pour France Grilles

DIRAC [DIRAC] [TSA-08] is a software framework for building distributed computing systems. It was primarily designed forthe needs of the LHCb [LHCb] Collaboration, and is now used by many other communities within EGI [EGI] as a primary wayof accessing grid resources. In France, dedicated instances of the service have been deployed in different locations toanswer specific needs. Building upon this existing expertise, France Grilles [FG] initiated last year a project to deploy anational, multi-community instance in order to share expertise and provide a consistent high-quality service. After describingDIRAC main aims and functionalities, this paper presents the motivations for such a project, as well as the wholeorganizational and technical process that led to the establishment of a production instance that already serves 13communities: astro.vo.eu-egee.org, biomed, esr, euasia, gilda, glast.org, prod.vo.eu-eela.eu, superbvo.org,vo.formation.idgrilles.fr, vo.france-asia.org, vo.france-grilles.fr, vo.msfg.fr and vo.mcia.fr

Joint Bayesian Deconvolution And Point Spread Function Estimation For Ultrasound Imaging

International audienceThis paper addresses the problem of blind deconvolution for ultrasound images within a Bayesian framework. The prior of the unknown ultrasound image to be estimated is assumed to be a product of generalized Gaussian distributions. The point spread function of the system is also assumed to be unknown and is assigned a Gaussian prior distribution. These priors are combined with the likelihood function to build the joint posterior distribution of the image and PSF. However, it is difficult to derive closed-form expressions of the Bayesian estimators associated with this posterior. Thus, this paper proposes to build estimators of the unknown model parameters from samples generated according to the model posterior using a hybrid Gibbs sampler. Simulation results performed on synthetic data allow the performance of the proposed algorithm to be appreciated