InforLorV4, Main, Merge, bibRecord, 003712

Grid Analysis of Radiological Data

Identifieur interne : 003712 ( Main/Merge ); précédent : 003711; suivant : 003713

Grid Analysis of Radiological Data

Auteurs : Cecile Germain-Renaud [France] ; Vincent Breton [France] ; Patrick Clarysse [France] ; Bertrand Delhay [France] ; Yann Gaudeau [France] ; Tristan Glatard [France] ; Emmanuel Jeannot [France] ; Yannick Legre [France] ; Johan Montagnat [France] ; Jean-Marie Moureaux [France] ; Angel Osorio [France] ; Xavier Pennec [France] ; Joel Schaerer [France] ; Romain Texier [France]

Source :

RBID : Hal:hal-00683992

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.

Url:

https://hal.archives-ouvertes.fr/hal-00683992

DOI: 10.4018/978-1-60566-374-6.ch019

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Hal:hal-00683992

Le document en format XML

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<front><div type="abstract" xml:lang="en">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.</div>
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Grid Analysis of Radiological Data

Grid Analysis of Radiological Data

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