A Robust Image Registration Interface for Large Volume Brain Atlas

Accurately mapping brain structures in three-dimensions is critical for an in-depth understanding of brain functions. Using the brain atlas as a hub, mapping detected datasets into a standard brain space enables efficient use of various datasets. However, because of the heterogeneous and nonuniform brain structure characteristics at the cellular level introduced by recently developed high-resolution whole-brain microscopy techniques, it is difficult to apply a single standard to robust registration of various large-volume datasets. In this study, we propose a robust Brain Spatial Mapping Interface (BrainsMapi) to address the registration of large-volume datasets by introducing extracted anatomically invariant regional features and a large-volume data transformation method. By performing validation on model data and biological images, BrainsMapi achieves accurate registration on intramodal, individual, and multimodality datasets and can also complete the registration of large-volume datasets (approximately 20 TB) within 1 day. In addition, it can register and integrate unregistered vectorized datasets into a common brain space. BrainsMapi will facilitate the comparison, reuse and integration of a variety of brain datasets.