  This paper explores the use of self-supervised deep learning in medical
imaging in cases where two scan modalities are available for the same subject.
Specifically, we use a large publicly-available dataset of over 20,000 subjects
from the UK Biobank with both whole body Dixon technique magnetic resonance
(MR) scans and also dual-energy x-ray absorptiometry (DXA) scans. We make three
contributions: (i) We introduce a multi-modal image-matching contrastive
framework, that is able to learn to match different-modality scans of the same
subject with high accuracy. (ii) Without any adaption, we show that the
correspondences learnt during this contrastive training step can be used to
perform automatic cross-modal scan registration in a completely unsupervised
manner. (iii) Finally, we use these registrations to transfer segmentation maps
from the DXA scans to the MR scans where they are used to train a network to
segment anatomical regions without requiring ground-truth MR examples. To aid
further research, our code will be made publicly available.
