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Towards Portable Large-Scale Image Processing with High-Performance Computing.

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  • 1Electrical Engineering, Vanderbilt University, 2201 West End Ave, Nashville, TN, 37235, USA. yuankai.huo@vanderbilt.edu.

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Summary
This summary is machine-generated.

Containerization enhances medical image computing infrastructure, improving portability and sustainability for large-scale data processing. This innovation addresses previous deployment challenges for the Vanderbilt University Institute for Imaging Science Center for Computational Imaging.

Keywords:
ContainerizedDAXLarge-scalePortableVUIISXNAT

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Area of Science:

  • Medical Image Computing
  • High-Performance Computing (HPC)
  • Bioinformatics Infrastructure

Background:

  • Large-scale medical image computing requires robust HPC infrastructure for data storage, job distribution, and processing.
  • The Vanderbilt University Institute for Imaging Science (VUIIS) Center for Computational Imaging (CCI) developed an integrated system using eXtensible Neuroimaging Archive Toolkit (XNAT), Distributed Automation for XNAT (DAX), and encapsulated processing pipelines ('spiders').
  • Initial native deployment on HPC faced portability and sustainability issues due to hardware variations and software dependencies.

Purpose of the Study:

  • To address challenges in deploying and maintaining large-scale medical image computing infrastructure.
  • To leverage containerization techniques for improved portability, sustainability, and flexibility.
  • To enhance the scalability and compatibility of image processing pipelines across different environments.

Main Methods:

  • Implemented containerization for the XNAT/DAX platform to isolate the medical image data storage and processing infrastructure.
  • Utilized containerization to encapsulate image processing pipelines ('spiders') and manage software dependencies.
  • Ensured compatibility with both High-Performance Computing (HPC) clusters and local workstations.

Main Results:

  • Achieved multi-level portability from system to application levels.
  • Enabled flexible and dynamic software development and expansion.
  • Facilitated scalable deployment of image processing pipelines ('spiders') across diverse computing environments.
  • Successfully processed nearly half-million medical image volumes.

Conclusions:

  • Containerization with XNAT/DAX significantly enhances the portability, sustainability, and flexibility of large-scale medical image computing infrastructure.
  • The new approach resolves issues related to deployment across varied HPC environments and software dependency management.
  • This containerized solution supports scalable deployment on HPC clusters and local workstations, paving the way for more efficient medical image analysis.