Deformable registration of multi-modal microscopic images using a pyramidal interactive registration-learning methodology

Co-registration of multi-modal microscopic images can integrate benefits of each modality, yet major challenges come from inherent difference between staining, distortions of specimens and various artefacts. In this paper, we propose a new interactive registration-learning method to register functional fluorescence (IF) and structural histology (HE) images in a pyramidal fashion. We synthesize HE image from the multi-channel IF image using a supervised machine learning technique and hence reduce the multi-modality registration problem into a mono-modality one, in which case the normalised cross correlation is used as the similarity measure. Unlike conventional applications of supervised learning, our classifier is not trained by 'ground-truth' (perfectly-registered) training dataset, as they are not available. Instead, we use a relatively noisy training dataset (affinely-registered) as an initialization and rely on the robustness of machine learning to the outliers and label updates via pyramidal deformable registration to gain better learning and predictions. In this sense, the proposed methodology has potential to be adapted in other learning problems as the manual labelling is usually imprecise and very difficult in the case of heterogeneous tissues.