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Recent successes in deep learning based deformable image registration (DIR)methods have demonstrated that complex deformation can be learnt directly fromdata while reducing computation time when compared to traditional methods.However, the reliance on fully linear convolutional layers imposes a uniformsampling of pixel/voxel locations which ultimately limits their performance. Toaddress this problem, we propose a novel approach of learning a continuous warpof the source image. Here, the required deformation vector fields are obtainedfrom a concatenated linear and non-linear convolution layers and a learnablebicubic Catmull-Rom spline resampler. This allows to compute smooth deformationfield and more accurate alignment compared to using only linear convolutionsand linear resampling. In addition, the continuous warping technique penalizesdisagreements that are due to topological changes. Our experiments demonstratethat this approach manages to capture large non-linear deformations andminimizes the propagation of interpolation errors. While improving accuracy themethod is computationally efficient. We present comparative results on a rangeof public 4D CT lung (POPI) and brain datasets (CUMC12, MGH10).

Conv2Warp: An unsupervised deformable image registration with continuous convolution and warping