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Diffeomorphic deformable multi-modal image registration is a challenging taskwhich aims to bring images acquired by different modalities to the samecoordinate space and at the same time to preserve the topology and theinvertibility of the transformation. Recent research has focused on leveragingdeep learning approaches for this task as these have been shown to achievecompetitive registration accuracy while being computationally more efficientthan traditional iterative registration methods. In this work, we propose asimple yet effective unsupervised deep learning-based {\em multi-modal} imageregistration approach that benefits from auxiliary information coming from thegradient magnitude of the image, i.e. the image edges, during the training. Theintuition behind this is that image locations with a strong gradient areassumed to denote a transition of tissues, which are locations of highinformation value able to act as a geometry constraint. The task is similar tousing segmentation maps to drive the training, but the edge maps are easier andfaster to acquire and do not require annotations. We evaluate our approach inthe context of registering multi-modal (T1w to T2w) magnetic resonance (MR)brain images of different subjects using three different loss functions thatare said to assist multi-modal registration, showing that in all cases theauxiliary information leads to better results without compromising the runtime.

Multi-modal unsupervised brain image registration using edge maps