Seamless neuroimaging visualization: The NiiVue wrapper ecosystem

Interactive visualization is essential for collaborative analysis of complex neuroimaging data. Modern workflows span multiple computing environments including notebooks, integrated development environments, and standalone applications. All these workflows rely on visualization for quality control, exploration, and interpretation. However, visualization tools are typically siloed, forcing users to switch between environments and disrupting interactive analysis. Here, we address this challenge by integrating a shared visualization module directly into these diverse environments, providing a consistent visualization experience across formats, representations, and usage contexts. We developed a generalizable ecosystem of open-source tools built around the NiiVue rendering engine, supporting four primary use cases. First, we provide integrated file viewer capability for VS Code and JupyterLab. Second, we showcase three different architectures for standalone desktop applications. Third, we integrate NiiVue for Python notebooks, Julia, and R. Finally, we integrate NiiVue into traditional web applications. This unified ecosystem enables immediate, in-place inspection of neuroimaging data across the majority of modern computing platforms, eliminating the need to export data to external viewers. The tools support interactive inspection, dashboard creation, and the development of custom analysis applications within users’ existing workflows. The components are validated by automated continuous-integration testing, including unit tests and browser visualization tests against stored reference targets. By embedding visualization directly into commonly used computing environments, this ecosystem lowers the barrier to interactive data exploration and streamlines neuroimaging workflows. Visualization becomes an integral component of automated, reproducible, and shareable analysis pipelines, supporting more efficient collaboration and interpretation of complex neuroimaging data. By sharing a common rendering core, file format support, and interaction model, users can transfer visualization skills seamlessly across environments.