Mapping Mouse Brain Slice Sequence to a Reference Brain Without 3D Reconstruction

Histological brain slices are widely used in neuroscience to study anatomical organization of neural circuits. Since data from many brains are collected, mapping the slices to a reference atlas is often the first step in interpreting results. Most existing methods rely on an initial reconstruction of the volume before registering it to a reference atlas. Because these slices are prone to distortion during sectioning process and often sectioned with nonstandard angles, reconstruction is challenging and often inaccurate. We propose a framework that maps each slice to its corresponding plane in the atlas to build a plane-wise mapping and then perform 2D nonrigid registration to build pixel-wise mapping. We use the L2 norm of the Histogram of Oriented Gradients (HOG) of two patches as the similarity metric for both steps, and a Markov Random Field formulation that incorporates tissue coherency to compute the nonrigid registration. To fix significantly distorted regions that are misshaped or much smaller than the control grids, we trained a context-aggregation network to segment and warp them to their corresponding regions with thin plate spline. We have shown that our method generates results comparable to an expert neuroscientist and is significantly better than reconstruction-first approaches.