Abstract
Brain atlases enable the mapping of labeled cells and projections from different brains onto a standard coordinate system. We address two issues in the construction and use of atlases. First, expert neuroanatomists ascertain the fine-scale pattern of brain tissue, the 'texture' formed by cellular organization, to define cytoarchitectural borders. We automate the processes of localizing landmark structures and alignment of brains to a reference atlas using machine learning and training data derived from expert annotations. Second, we construct an atlas that is active; that is, augmented with each use. We show that the alignment of new brains to a reference atlas can continuously refine the coordinate system and associated variance. We apply this approach to the adult murine brainstem and achieve a precise alignment of projections in cytoarchitecturally ill-defined regions across brains from different animals.