November 15, 2019

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) ( 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”.


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

Travel scholarships available, apply before October 4th, 2019


Thomas Close from Monash Biomedical Imaging


Registration is available here.

What is the CVL?

The Characterisation Virtual Laboratory is a free cloud-based virtual desktop workbench to perform analysis of complex image and microscopy data. It serves to run analysis in a large computing infrastructure, all embedded in a web browser accessible using AAF (Australian Access Federation), and connected to a HPC (high performance computing) infrastructure. For more information please visit

What software tools are available?

About 800 different versions of tools for image analysis and visualisation are available through the CVL. A Virtual Laboratory, such as the CVL can save researchers time as they do not have to create and maintain their own online environments and tools.

Virtual Desktop

Currently the virtual desktop service is provided by MASSIVE (Multi-Modal Australian Sciences Imaging and Visualisation Environment). To get started with the CVL at MASSIVE please follow the instructions here.

Contact for General enquiries about the CVL@MASSIVE

Email: MASSIVE helpdesk