Brain atlases like the Allen Mouse Brain Common Coordinate Framework (CCFv3) are essential tools to guide the integration of different sources of neuroscience data. In this study, we introduce a novel method for curating brain atlas regions that leverages the strength of Houdini and Python software. This approach combines industry-standard graphic libraries with a dynamic user interface. Anatomists can interactively visualise, slice, annotate and analyse brain regions with high reproducibility. In Houdini, readability and traceability are ensured by simultaneously displaying results as a 3D object and a series of code steps in an editable flowchart. From the shape and lamination of the selected region, robust algorithms can be used to generate geometrical properties such as local orientation fields and parametric coordinate systems. These metrics provide a richer description of space that can be used to construct atlas-based circuit models. Brain regions with complex shapes such as the hippocampus and cerebellum benefit especially from our approach. We present an augmented version of the mouse hippocampus of CCFv3 that demonstrates the effectiveness of our method on one of the most challenging anatomical regions. This approach represents a significant advancement in brain atlasing, providing researchers with unparalleled capabilities for exploration, analysis, and refinement of existing atlases in a user-friendly, resource-efficient and clear workflow.