Grid Analysis of Radiological Data

Cecile Germain-Renaud 1, 2 Vincent Breton 3 Patrick Clarysse 4 Bertrand Delhay 4 Yann Gaudeau 5 Tristan Glatard 4 Emmanuel Jeannot 6 Yannick Legre 7 Johan Montagnat 8 Jean-Marie Moureaux 5 Angel Osorio 9 Xavier Pennec 10 Joel Schaerer 4 Romain Texier 8
2 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, INRIA Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
3 Equipe PCSV
LPC - Laboratoire de Physique Corpusculaire [Clermont-Ferrand]
6 ALGORILLE - Algorithms for the Grid
LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications, INRIA Lorraine
7 HealthGrid
LPC - Laboratoire de Physique Corpusculaire [Clermont-Ferrand]
8 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe MODALIS
SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
10 ASCLEPIOS - Analysis and Simulation of Biomedical Images
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Grid technologies and infrastructures can contribute to harnessing the full power of computer-aided image analysis into clinical research and practice. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. This chapter reports on the goals, achievements and lessons learned from the AGIR (Grid Analysis of Radiological Data) project. AGIR addresses this challenge through a combined approach. On one hand, leveraging the grid middleware through core grid medical services (data management, responsiveness, compression, and workflows) targets the requirements of medical data processing applications. On the other hand, grid-enabling a panel of applications ranging from algorithmic research to clinical use cases both exploits and drives the development of the services.
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Book section
Mario Cannataro (Ed.). Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine and Healthcare, IGI, pp.363-391, 2009, chapter 19, <10.4018/978-1-60566-374-6.ch019>


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Cecile Germain-Renaud, Vincent Breton, Patrick Clarysse, Bertrand Delhay, Yann Gaudeau, et al.. Grid Analysis of Radiological Data. Mario Cannataro (Ed.). Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine and Healthcare, IGI, pp.363-391, 2009, chapter 19, <10.4018/978-1-60566-374-6.ch019>. <hal-00683992>

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