BrainPainter: A software for the visualisation of brain structures, biomarkers and associated pathological processes
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Abstract (arXiv)
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 is customisable, easy to use, and can run straight from the web browser: https://brainpainter.csail.mit.edu , as well as from source-code packaged in a docker container: https://github.com/mrazvan22/brain-coloring . 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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 May 2019.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 3.
4 papers.
- 2025OCL: Ordinal Contrastive Learning for Imputating Features with Progressive Labelscited by 6
- 2023Deep Geometric Learning with Monotonicity Constraints for Alzheimer's Disease Progressioncited by 4
- 2025Modality-Agnostic Style Transfer for Holistic Feature Imputationcited by 3
- 2025Learning Covariance-Based Multi-Scale Representation of Neuroimaging Measures for Alzheimer Classificationcited by 2
- 2025OCL: Ordinal Contrastive Learning for Imputating Features with Progressive Labels3 Mar 2025
- 2025Modality-Agnostic Style Transfer for Holistic Feature Imputation3 Mar 2025
- 2025Learning Covariance-Based Multi-Scale Representation of Neuroimaging Measures for Alzheimer Classification3 Mar 2025
- 2023Deep Geometric Learning with Monotonicity Constraints for Alzheimer's Disease Progression5 Oct 2023