Anatomy-aware, label-informed approach improves image registration for challenging datasets

Abstract

Image registration-based volumetric morphometrics have emerged as a valuable method for identifying subtle morphological differences in neuroimaging and other biomedical images. However, accurate registration out-of-the-box remains challenging when overt morphological phenotypes--such as those observed in developmental and comparative studies--are present in a dataset. A new label-informed image registration function developed in the ANTsX ecosystem provides an easy to use, generalizable solution for anatomy-aware registration of a wide diversity of morphological variation. In this approach, segmentations (i.e., labels) provide a priori regional correspondences that guide the registration. These labels can be generated by any method-manually, using semi-automated tools, or through deep learning-based approaches-and allow morphological experts to define regions of correspondence based on biological concepts of homology (e.g., tissue origin, gene expression patterns). Here we demonstrate the utility of this label-informed image registration approach for improving the registration knockout mouse embryos which fail to register to a wildtype (normative) template image by traditional registration methods. E15.5 Gli2-/- mouse embryos show a severe scoliosis and radical topological rearrangement of the internal organs. Compared to traditional, intensity-only registration, the new label-informed image registration improved the correspondence of knockout subjects to the canonical template image, which resulted in increased power and sensitivity of downstream statistical analyses. All in all, label-informed image registration provides a flexible and customizable method to allow image registration in datasets for which registration-based morphometrics were previously unfeasible, unlocking new potential applications of registration-based morphometrics in developmental, comparative, and evolutionary studies.