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Current approaches for deformable medical image registration often struggleto fulfill all of the following criteria: versatile applicability, smallcomputation or training times, and the being able to estimate largedeformations. Furthermore, end-to-end networks for supervised training ofregistration often become overly complex and difficult to train. For theLearn2Reg2021 challenge, we aim to address these issues by decoupling featurelearning and geometric alignment. First, we introduce a new very fast andaccurate optimisation method. By using discretised displacements and a coupledconvex optimisation procedure, we are able to robustly cope with largedeformations. With the help of an Adam-based instance optimisation, we achievevery accurate registration performances and by using regularisation, we obtainsmooth and plausible deformation fields. Second, to be versatile for differentregistration tasks, we extract hand-crafted features that are modality andcontrast invariant and complement them with semantic features from atask-specific segmentation U-Net. With our results we were able to achieve theoverall Learn2Reg2021 challenge's second place, winning Task 1 and being secondand third in the other two tasks.

Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021