  Registration is an important part of many clinical workflows and factually,
including information of structures of interest improves registration
performance. We propose a novel approach of combining segmentation information
in a registration framework using self supervised segmentation feature maps
extracted using a pre-trained segmentation network followed by clustering.
Using self supervised feature maps enables us to use segmentation information
despite the unavailability of manual segmentations. Experimental results show
our approach effectively replaces manual segmentation maps and demonstrate the
possibility of obtaining state of the art registration performance in real
world cases where manual segmentation maps are unavailable.
