Self-Supervised Multi-Modal Alignment for Whole Body Medical Imaging
This work addresses the challenge of multi-modal alignment in medical imaging for researchers and clinicians, offering an unsupervised method to improve scan analysis, though it is incremental as it builds on existing self-supervised techniques.
The paper tackles the problem of aligning whole body medical scans from different modalities (MR and DXA) by introducing a self-supervised contrastive framework that learns to match scans with high accuracy, enabling automatic cross-modal registration and segmentation transfer without ground-truth MR data, using a dataset of over 20,000 subjects.
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.