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BrainPainter: A Software for the Visualisation of Brain Structures, Biomarkers and Associated Pathological Processes

  • Răzvan V. MarinescuEmail author
  • Arman Eshaghi
  • Daniel C. Alexander
  • Polina Golland
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11846)

Abstract

We present BrainPainter, a software that automatically generates images of highlighted brain structures given a list of numbers corresponding to the output colours of each region. Compared to existing visualisation software (i.e. Freesurfer, SPM, 3D Slicer), BrainPainter has three key advantages: (1) it does not require the input data to be in a specialised format, allowing BrainPainter to be used in combination with any neuroimaging analysis tools, (2) it can visualise both cortical and subcortical structures and (3) it can be used to generate movies showing dynamic processes, e.g. propagation of pathology on the brain. We highlight three use cases where BrainPainter was used in existing neuroimaging studies: (1) visualisation of the degree of atrophy through interpolation along a user-defined gradient of colours, (2) visualisation of the progression of pathology in Alzheimer’s disease as well as (3) visualisation of pathology in subcortical regions in Huntington’s disease. Moreover, through the design of BrainPainter we demonstrate the possibility of using a powerful 3D computer graphics engine such as Blender to generate brain visualisations for the neuroscience community. Blender’s capabilities, e.g. particle simulations, motion graphics, UV unwrapping, raster graphics editing, raytracing and illumination effects, open a wealth of possibilities for brain visualisation not available in current neuroimaging software. BrainPainter (Source code: https://github.com/mrazvan22/brain-coloring) is customisable, easy to use, and can run straight from the web browser: http://brainpainter.csail.mit.edu. It can be used to visualise biomarker data from any brain imaging modality, or simply to highlight a particular brain structure for e.g. anatomy courses.

Notes

Acknowledgements

RVM was supported by the NIH grants NIBIB NAC P41EB015902 and NINDS R01NS086905, as well as the EPSRC Centre For Doctoral Training in Medical Imaging with grant EP/L016478/1. AE received a McDonald Fellowship from the Multiple Sclerosis International Federation (MSIF, www.msif.org), and the ECTRIMS – MAGNIMS Fellowship. DCA was supported by EuroPOND, which is an EU Horizon 2020 project, and by EPSRC grants J020990, M006093 and M020533. PG was supported by NIH grants NIBIB NAC P41EB015902 and NINDS R01NS086905.

We are also particularly grateful to Anderson Winkler for creating the 3D brain templates for all three atlases, which are used in this work (https://brainder.org/research/brain-for-blender/).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Răzvan V. Marinescu
    • 1
    • 2
    Email author
  • Arman Eshaghi
    • 2
    • 3
  • Daniel C. Alexander
    • 2
  • Polina Golland
    • 1
  1. 1.Computer Science and Artificial Intelligence LaboratoryMITCambridgeUSA
  2. 2.Centre for Medical Image ComputingUniversity College LondonLondonUK
  3. 3.Queen Square MS CentreUCL Institute of NeurologyLondonUK

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