An Active Texture-based Atlas for Automated Mapping of Structures and Markers Across Brains
An anatomical atlas constitutes a quantitative description of the structural organization of an organism. Nissl-stained serial brain sections reveal cellular texture (cytoarchitecture) and are the gold standard for defining structures. As high-throughput techniques advance and automated registration becomes commonplace, the role of an atlas grows beyond a static map towards a repository where a wide variety of experiment data acquired from different subjects can be integrated in a common template defined in a standard coordinate system. For histological data (0.5 micron resolution), however, the reliance of intensity-based registration algorithms on downsampled pixel/voxel intensities (> 10 micron) and the resulting negligence of fine-scale textures means they often fail to accurately align structures whose boundaries are defined by cytoarchitecture rather than graylevel, such as many brainstem nuclei. The lack of a registration tool that strongly utilizes texture and a nucleus-level atlas to assist such registration has stifled comparison of results across experiments.
We demonstrate a data-driven active atlas system that automatically aligns brains based on cytoarchitectural landmarks. Our approach combines discriminative texture detectors based on CNN features with a reference atlas that describes structure shapes as probabilistic volumes and their locations as Gaussian distributions. Histological serial sections are reconstructed in 3-D and converted to structure probability maps by classifiers trained to differentiate the texture inside versus outside each structure. Registration is achieved by maximization of the correlation between the probability maps and the reference atlas using a global affine transform followed by deformation interpolated from structure-specific rigid transforms. Initialized from annotation by expert neuroanatomists, the atlas is continuously refined after incorporation of new brains in a semi-supervised fashion.
Based on automated registration of twelve specimens, we developed an atlas for adult murine brainstem which defines 28 distinct structures in both hemispheres. The system's utility in advancing brain circuitry study is demonstrated by the precise mapping of neuronal projections in cytoarchitecturally ill-defined regions across brains from different animals. Quantitative results showed that our approach produced accurate and confident registration and significantly reduces human labor.