Model-Based Refinement of Nonlinear Registrations in 3D Histology Reconstruction

Recovering the 3D structure of a stack of histological sections (3D histology reconstruction) requires a linearly aligned reference volume in order to minimize z-shift and “banana effect”. Reconstruction can then be achieved by computing 2D registrations between each section and its corresponding resampled slice in the volume. However, these registrations are often inaccurate due to their inter-modality nature and to the strongly nonlinear deformations introduced by histological processing. Here we introduce a probabilistic model of spatial deformations to efficiently refine these registrations, without the need to revisit the imaging data. Our method takes as input a set of nonlinear registrations between pairs of 2D images (within or across modalities), and uses Bayesian inference to estimate the most likely spanning tree of latent transformations that generated the measured deformations. Results on synthetic and real data show that our algorithm can effectively 3D reconstruct the histology while being robust to z-shift and banana effect. An implementation of the approach, which is compatible with a wide array of existing registration methods, is available at JEI’s website: www.jeiglesias.com.