A Python library for creating high-quality 3D visualizations of neuroanatomical data in atlas space.
brainrender is a Python library for creating high-quality 3D visualizations of neuroanatomical data registered to brain atlas space. It solves the problem of visualizing complex experimental data in the context of standardized anatomical references, enabling researchers to explore and present their findings effectively.
Neuroscientists and researchers working with neuroanatomical data who need to visualize experimental results in 3D brain atlas space for analysis and publication.
Developers choose brainrender for its user-friendly interface, seamless integration with public brain atlases, and ability to generate publication-ready 3D renderings without requiring extensive programming or visualization expertise.
A Python package to visualise neuroanatomical data in atlas space
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Directly incorporates data from publicly available atlases like the Allen Mouse Brain Atlas, eliminating manual registration efforts for standardized neuroanatomical contexts.
Enables visualization of user-generated data such as cell coordinates or traces registered to atlas space, as shown in the quickstart example with Points actors.
Generates high-quality 3D renderings suitable for scientific publications, with customizable aesthetics for clear communication of complex findings.
Offers a user-friendly interface for creating scenes and adding brain regions, lowering the barrier for neuroscientists without extensive programming expertise.
Primarily supports mouse brain atlases like Allen Mouse Brain; researchers working with other species or custom atlases may find integration challenging or unsupported.
Requires a local Python environment and graphical backend, making it less suitable for web-based or cloud deployments without complex setup.
3D rendering of extensive data points can be computationally intensive, potentially slowing down on standard hardware and limiting real-time exploration.