Deformable registration of immunofluorescence and histology using iterative cross-modal propagation

In this work, we cast a multi-modal immunofluorescence to histology registration problem is cast to a mono-modal one by iteratively propagating tissue-specific features from one modality to generate intensities in the other modality via an implicitly learnt random-forest regression framework. The proposed method iterates between modality propagation and image registration in a unified formulation. Evaluations on real and simulated tissue deformations establish superiority of the proposed work over comparative methods in handling highly complex intermodal intensity relationships. This framework will aid in quantitative analysis of the tissue structure, functionality thus providing for co-located molecular validations and bringing in spatial fidelity to histological assessment.