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A midas plugin to enable construction of reproducible web-based image processing pipelines.

Michael Grauer1, Patrick Reynolds1, Marion Hoogstoel2

  • 1Kitware, Inc. Carrboro NC, USA.

Frontiers in Neuroinformatics
|January 14, 2014
PubMed
Summary

This study introduces a web platform that simplifies brain image processing for neuroscience and biomedical researchers. The tool enables experts to build pipelines, making advanced image analysis accessible to those with limited experience, promoting collaboration and reproducible science.

Keywords:
MRIautomated pipelinesbrain image processingrodent imagingworkflow processing

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Scientific Computing

Background:

  • Image processing is crucial for neuroscience research but presents a steep learning curve for non-experts.
  • Existing tools often lack accessibility for researchers without specialized image processing knowledge.
  • There is a need for user-friendly platforms that facilitate complex image analysis in biomedical research.

Purpose of the Study:

  • To present a web-based platform that democratizes brain image processing for neuroscience and biomedical researchers.
  • To enable experts to create and share image processing pipelines, making them accessible to users with limited expertise.
  • To foster scientific collaboration and reproducible research through shared datasets and processing workflows.

Main Methods:

  • Development of a Midas plugin for creating and executing image processing pipelines.
  • Utilization of grid computing platforms (BatchMake, HTCondor) for pipeline execution.
  • Implementation of user-friendly interfaces for pipeline construction, job management, and result visualization.
  • Application of ITK-based workflows for diffusion-weighted MRI (DW MRI) of rodent brains.

Main Results:

  • Successful creation and execution of ITK-based image processing workflows for rodent brain DW MRI.
  • Demonstration of a streamlined user interface and simplified pipeline management.
  • Validation of the platform's capability to support collaborative research and data sharing.
  • Evidence of simplified troubleshooting, centralized maintenance, and easy data sharing.

Conclusions:

  • The developed Midas plugin significantly enhances accessibility to advanced brain image processing for biomedical researchers.
  • The platform facilitates reproducible science by enabling the sharing of datasets and processing pipelines.
  • This innovative tool supports scientific collaboration and streamlines complex image analysis tasks.
  • The plugin's architecture is versatile and can support various executable or script-based processing pipelines beyond neuroimaging.