  Recent studies demonstrated the eligibility of convolutional neural networks
(CNNs) for solving the image registration problem. CNNs enable faster
transformation estimation and greater generalization capability needed for
better support during medical interventions. Conventional fully-supervised
training requires a lot of high-quality ground truth data such as
voxel-to-voxel transformations, which typically are attained in a too tedious
and error-prone manner. In our work, we use weakly-supervised learning, which
optimizes the model indirectly only via segmentation masks that are a more
accessible ground truth than the deformation fields. Concerning the weak
supervision, we investigate two segmentation similarity measures: multiscale
Dice similarity coefficient (mDSC) and the similarity between
segmentation-derived signed distance maps (SDMs). We show that the combination
of mDSC and SDM similarity measures results in a more accurate and natural
transformation pattern together with a stronger gradient coverage. Furthermore,
we introduce an auxiliary input to the neural network for the prior information
about the prostate location in the MR sequence, which mostly is available
preoperatively. This approach significantly outperforms the standard two-input
models. With weakly labelled MR-TRUS prostate data, we showed registration
quality comparable to the state-of-the-art deep learning-based method.
