Diffeomorphic registration with intensity transformation and missing data: Application to 3D digital pathology of Alzheimer\'s disease

This paper examines the problem of diffeomorphic image mapping in the presence of differing image intensity profiles and missing data. Our motivation comes from the problem of aligning 3D brain MRI with 100 micron isotropic resolution, to histology sections with 1 micron in plane resolution. Multiple stains, as well as damaged, folded, or missing tissue are common in this situation. We overcome these challenges by introducing two new concepts. Cross modality image matching is achieved by jointly estimating polynomial transformations of the atlas intensity, together with pose and deformation parameters. Missing data is accommodated via a multiple atlas selection procedure where several atlases may be of homogeneous intensity and correspond to ``background99 or ``artifact99. The two concepts are combined within an Expectation Maximization algorithm, where atlas selection posteriors and deformation parameters are updated iteratively, and polynomial coefficients are computed in closed form. We show results for 3D reconstruction of digital pathology and MRI in standard atlas coordinates. In conjunction with convolutional neural networks, we quantify the 3D density distribution of tauopathy throughout the medial temporal lobe of an Alzheimer9s disease postmortem specimen.