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.

本文探讨在医学成像中使用自监督深度学习的应用，特别是在存在两种扫描模式的同一主体的情况下。通过使用来自英国生物库的超过 20,000 个主体的完整身体迪克森技术磁共振（MR）扫描和双能 X 射线吸收计量（DXA）扫描的大型公开数据集，作者提出了一种针对多模式图像匹配对比框架，并且可以在没有监督的情况下执行自动跨模式扫描配准。最后，作者使用这些配准将 DXA 扫描的分割图传输到 MR 扫描中，在不需要地面真实 MR 样本的情况下，训练可以分割解剖区域的网络。