Paula Andrea Martinez
Paula Andrea Martinez National Training Coordinator - Characterisation Community

Automating neuroimaging analysis workflows with Nipype, Arcana and Banana - Invitation

Automating neuroimaging analysis workflows with Nipype, Arcana and Banana - Invitation

Last updated 28 August 2019

Hands-on workshop Date: 15 November, 2019 (after the VBIC Annual Network Meeting) Duration: 1 day (9:30 - 5:00) Proposed Location: Melbourne, TBA Instructor: Thomas Close from Monash Biomedical Imaging

This is an invitation for a hands on workshop, travel scholarships are available (apply before Oct 4th). If you would like more information about this, please get in touch.

Automating neuroimaging analysis workflows with Nipype, Arcana and Banana

Analysis of neuroimaging-research data involves the sequential application of algorithms implemented in a number of heterogeneous toolkits (e.g. FSL, SPM, MRTrix, ANTs, AFNI, DiPy). This makes constructing complete workflows challenging as it requires not only the relevant scientific knowledge but also familiarity with the syntax and options of each of the tools involved.

The workshop will show how to wrap neuroimaging tools within consistent interfaces and link them together into robust workflows using the Nipype Python package. Participants will then be shown how common components of these analysis workflows can be consolidated within object-oriented base classes using the Abstraction of Repository Centric ANAlysis (Arcana) (http://arcana.readthedocs.io) framework, and how this is used in the Brain imAgiNg Analysis iN Arcana (Banana) package to capture the arcana (obscure knowledge) of neuroimaging analysis workflow design.

In the last part of the course, participants will learn how to extend and customise the classes in Banana to the specific needs of their own analysis, and apply these workflows to project data stored in BIDS datasets. Then finally, how workflows can be automated for data stored in XNAT repositories by encapsulating them within Docker containers and using XNAT’s “container service”.

Registration link.

Travel scholarships are available, apply before Oct 4th, follow the link for instructions on how to apply.

Pre-requisites

  • Proficiency in Python programming, or programming in general and familiarity with object-oriented concepts.
  • A conceptual understanding of container technology (i.e. Docker/Singularity) would be beneficial.
  • Some familiarity with the function of standard neuroimaging toolkits (e.g. FSL, SPM, MRTrix, ANTs, AFNI, DiPy) would be good but not strictly necessary.
  • An account on MASSIVE/CVL.

Details

Characterisation Virtual Laboratory (CVL)

The CVL is a nationally funded software infrastructure collaboration to make scientific tools for image analysis and processing, available freely and cloud-ready. The CVL is also a community who support training and best data management practices.

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