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dc.contributor.authorMarinescu, Razvan V
dc.contributor.authorEshaghi, Arman
dc.contributor.authorAlexander, Daniel C.
dc.contributor.authorGolland, Polina
dc.date.accessioned2021-01-08T14:56:09Z
dc.date.available2021-01-08T14:56:09Z
dc.date.issued2019-10
dc.identifier.isbn9783030332259
dc.identifier.isbn9783030332266
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttps://hdl.handle.net/1721.1/129341
dc.descriptionPart of the Lecture Notes in Computer Science book series (LNCS, volume 11846)en_US
dc.description.abstractWe 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.en_US
dc.description.sponsorshipNIH (Grants NIBIB NAC P41EB015902 and NINDS R01NS086905)en_US
dc.language.isoen
dc.publisherSpringer International Publishingen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/978-3-030-33226-6_13en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleBrainPainter: A Software for the Visualisation of Brain Structures, Biomarkers and Associated Pathological Processesen_US
dc.typeBooken_US
dc.identifier.citationMarinescu, Răzvan V. et al. "BrainPainter: A Software for the Visualisation of Brain Structures, Biomarkers and Associated Pathological Processes." MBIA 2019, MFCA 2019: Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy, Lecture Notes in Computer Science, 11846, Springer International Publishing, 2019, 112-120. © 2019 Springer Nature Switzerlanden_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.relation.journalLecture Notes in Computer Scienceen_US
dc.eprint.versionOriginal manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2020-12-15T18:51:53Z
dspace.orderedauthorsMarinescu, RV; Eshaghi, A; Alexander, DC; Golland, Pen_US
dspace.date.submission2020-12-15T18:51:58Z
mit.journal.volume11846en_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusComplete


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