Data Augmentation for Automatic Identification of Spatiotemporal Dispersion Electrograms in Persistent Atrial Fibrillation Ablation Using Machine Learning.

Catheter ablation is increasingly used to treat atrial fibrillation (AF), the most common sustained cardiac arrhythmia encountered in clinical practice. A recent breakthrough finding in AF ablation consists in identifying ablation sites based on their spatiotemporal dispersion (STD). STD stands for a delay of the cardiac activation observed in intracardiac electrograms (EGMs) across contiguous leads.

OntoVIP: an ontology for the annotation of object models used for medical image simulation.

This paper describes the creation of a comprehensive conceptualization of object models used in medical image simulation, suitable for major imaging modalities and simulators. The goal is to create an application ontology that can be used to annotate the models in a repository integrated in the Virtual Imaging Platform (VIP), to facilitate their sharing and reuse. Annotations make the anatomical, physiological and pathophysiological content of the object models explicit.

A virtual imaging platform for multi-modality medical image simulation.

This paper presents the Virtual Imaging Platform (VIP), a platform accessible at http://vip.creatis.insa-lyon.

Technical support for Life Sciences communities on a production grid infrastructure.

Production operation of large distributed computing infrastructures (DCI) still requires a lot of human intervention to reach acceptable quality of service. This may be achievable for scientific communities with solid IT support, but it remains a show-stopper for others. Some application execution environments are used to hide runtime technical issues from end users.

Grid-wide neuroimaging data federation in the context of the NeuroLOG project.

Grid technologies are appealing to deal with the challenges raised by computational neurosciences and support multi-centric brain studies. However, core grids middleware hardly cope with the complex neuroimaging data representation and multi-layer data federation needs. Moreover, legacy neuroscience environments need to be preserved and cannot be simply superseded by grid services.

A distributed security policy for neuroradiology data sharing.

Grids are key technologies to federate data distributed in multiple neuroscience centers, thus enabling large scale multi-centric studies. However, the take up of these technologies is slow due to the difficulty to manipulate sensitive neuroradiological data in an open environment and the recognized risk of federated sites to loose control over their valuable data. In this paper we propose a distributed data access control policy, enabling the federation of existing data stores, where local security policies prevail, to supports multi-centric neuroscience studies.

Towards production-level cardiac image analysis with grids.

Production exploitation of cardiac image analysis tools is hampered by the lack of proper IT infrastructure in health institutions, the non trivial integration of heterogeneous codes in coherent analysis procedures, and the need to achieve complete automation of these methods. HealthGrids are promising technologies to address these difficulties. This paper details how they can be complemented by high level problem solving environments such as workflow managers to improve the performance of applications both in terms of execution time and robustness of results.

NeuroLOG: a community-driven middleware design.

The NeuroLOG project designs an ambitious neurosciences middleware, gaining from many existing components and learning from past project experiences. It is targeting a focused application area and adopting a user-centric perspective to meet the neuroscientists expectations. It aims at fostering the adoption of HealthGrids in a pre-clinical community.

Performance evaluation of grid-enabled registration algorithms using bronze-standards.

Evaluating registration algorithms is difficult due to the lack of gold standard in most clinical procedures. The bronze standard is a real-data based statistical method providing an alternative registration reference through a computationally intensive image database registration procedure. We propose in this paper an efficient implementation of this method through a grid-interfaced workflow enactor enabling the concurrent processing of hundreds of image registrations in a couple of hours only.

Medical image registration algorithms assesment: Bronze Standard application enactment on grids using the MOTEUR workflow engine.

Medical image registration is pre-processing needed for many medical image analysis procedures. A very large number of registration algorithms are available today, but their performance is often not known and very difficult to assess due to the lack of gold standard. The Bronze Standard algorithm is a very data and compute intensive statistical approach for quantifying registration algorithms accuracy.

Bridging clinical information systems and grid middleware: a Medical Data Manager.

This paper describes the effort to deploy a Medical Data Management service on top of the EGEE grid infrastructure. The most widely accepted medical image standard, DICOM, was developed for fulfilling clinical practice. It is implemented in most medical image acquisition and analysis devices.

Authentication and authorisation prototype on the mu grid for medical data management.

This paper presents mu grid, a light weight middleware for grid applications, and focuses mainly on security issues--more specifically on the access control to resources--that are critical for the gridification of many medical applications. For this purpose, we use Sygn as a distributed, certificate based, and flexible access control mechanism, which has been fully integrated in mu grid. We discuss the advantages of the solution compared to classical grid approaches and the limitations of the final architecture.

4D deformable models with temporal constraints: application to 4D cardiac image segmentation.

Segmentation of time series of 3D cardiac images is clinically used for the assessment of the mechanical function of the left ventricle. To take into account the 4D (3D+T) nature of those images, we propose to extend the deformable surface framework by introducing time-dependent constraints. Thus, in addition to computing an internal force for enforcing the regularity of the deformable model, prior motion knowledge is introduced in the deformation process through either temporal smoothing or trajectory constraints.