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OpenMOLE: a Workflow Engine for Distributed Medical Image Analysis

Abstract : This works demonstrates how the OpenMOLE platform can provide a straightforward way to distribute heavy workloads generated by medical imaging analysis. OpenMOLE allows its users to benefit from a large set of distributed computing infrastructures such as clusters or com-puting grids, no matter the kind of application they are running. Here we extend the OpenMOLE tools to two new cluster job schedulers: SLURM and Condor. We also contribute to the Yapa pack-aging tool to support the widely spread virtual environment package from the Python programming language. Our test case shows how our developments allow a medical imaging application to be distributed using the OpenMOLE toolkit.
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Contributor : Jonathan Passerat-Palmbach Connect in order to contact the contributor
Submitted on : Wednesday, January 7, 2015 - 4:15:16 PM
Last modification on : Tuesday, October 18, 2022 - 3:30:49 AM
Long-term archiving on: : Wednesday, June 3, 2015 - 12:46:33 PM


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Distributed under a Creative Commons Attribution 4.0 International License


  • HAL Id : hal-01099220, version 1


Jonathan Passerat-Palmbach, Mathieu Leclaire, Romain Reuillon, Zehan Wang, Daniel Rueckert. OpenMOLE: a Workflow Engine for Distributed Medical Image Analysis. International Workshop on High Performance Computing for Biomedical Image Analysis (part of MICCAI 2014), Sep 2014, Boston, United States. ⟨hal-01099220⟩



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