Mapping histological brain images to the Allen mouse brain atlas
Keywords: Electrical Engineering.

In this thesis, we present an automatic framework that maps a histological image sequence to the Allen Mouse Brain Atlas which is in use at multiple neuroscience labs. The method is stable and continues to work on noisy sectional brain data. We also describe applications of our framework on multiple biological studies consisting different kind of datasets advancing research in understanding neurobiological organization. Code and sample dataset are available at our project website: sites.google.com/view/brain-mapping. Histological brain slices imaged by high-resolution optical microscopy are widely used in neuroscience to study the anatomical organization of neural circuits. Systematic and accurate comparisons of anatomical data from multiple brains and from different studies can benefit tremendously from registering histological slices onto a common reference. To this end, the Allen Mouse and Human Brain Atlases have been created. However this registration task is extremely challenging due to heterogeneity of biological structures, image distortions and imaging artifacts introduced during the brain sectioning, staining, mounting, and imaging processes. Existing methods rely on an initial full or partial reconstruction of the experimental brain volume before registering a histological slice sequence to the reference, or extensive manual inspection is needed. Because these slices are often sectioned with non-standard angles, and without an external reference, curved structures end up straightened. Reconstruction is often inaccurate. Due to the low signal to noise ratio, traditional nonrigid image registration methods are not always reliable either. In this thesis, we describe a framework that completely solves the z-shift problem and produces stable and accurate registration between histological image sequences and the Allen Mouse Brain Atlas. This work first determines the cutting angle and finds the best matching slice of each histological brain image in the reference volume directly by leveraging brain structural characteristics and symmetry. After finding the plane-wise mapping, we then register every image pair - each experimental slice and its corresponding cutting planes in the Allen Mouse Brain Atlas - nonrigidly to build a pixel-wise mapping. We modify the standard Markov random field framework on medical image registration to model accumulated tension when deforming tissue to more naturally deal with the easily-deformed cavities throughout the brain. We directly place control points on the automatically extracted salient points avoiding excessive deformation. Both steps novelly use L2 norm of histogram of oriented gradients difference as the similarity metric. We have experimented our method on both simulated and experimental brains and on both sectional and full brains. Since our method is mostly automatic, and the accuracy is similar to or better than expert neuroscientists even for datasets where many slices are corrupted, our method is under use in different neuroscience labs of different institutions, making multi-brain histological data analysis possible and accurate, therefore advancing the research in understanding neurobiological organization.