Correspondence Transfer for the Registration of Multimodal Images

Gene expression data provide information on the location where certain genes are active; in order for this to be useful, such a location must be registered to an anatomical atlas. Because gene expression maps are considerably different from each other - they display the expression of different genes - and from the anatomical atlas, this problem is currently addressed either manually by trained experts, or by neglecting all image information and only using the pre-segmented boundaries. In this manuscript we concentrate on data discrepancy measures that take into account image information when this is present in both the target and template images. We exploit such "bi-lateral" structures to drive the correspondence process in regions where the intensity information is inconsistent, analogously to a "motion in- painting" task. Although no ground truth can be established, and prior information clearly plays a key role, we show that our model achieves desirable results on subjective tests validated by expert subjects.