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Recent studies demonstrated the eligibility of convolutional neural networks(CNNs) for solving the image registration problem. CNNs enable fastertransformation estimation and greater generalization capability needed forbetter support during medical interventions. Conventional fully-supervisedtraining requires a lot of high-quality ground truth data such asvoxel-to-voxel transformations, which typically are attained in a too tediousand error-prone manner. In our work, we use weakly-supervised learning, whichoptimizes the model indirectly only via segmentation masks that are a moreaccessible ground truth than the deformation fields. Concerning the weaksupervision, we investigate two segmentation similarity measures: multiscaleDice similarity coefficient (mDSC) and the similarity betweensegmentation-derived signed distance maps (SDMs). We show that the combinationof mDSC and SDM similarity measures results in a more accurate and naturaltransformation pattern together with a stronger gradient coverage. Furthermore,we introduce an auxiliary input to the neural network for the prior informationabout the prostate location in the MR sequence, which mostly is availablepreoperatively. This approach significantly outperforms the standard two-inputmodels. With weakly labelled MR-TRUS prostate data, we showed registrationquality comparable to the state-of-the-art deep learning-based method.

Learning Multi-Modal Volumetric Prostate Registration with Weak Inter-Subject Spatial Correspondence