Learning a Deformable Registration Pyramid
Published in In the proceedings of Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data, 2021
Abstract: We introduce an end-to-end unsupervised (or weakly supervised) image registration method that blends conventional medical image registration with contemporary deep learning techniques from computer vision. Our method downsamples both the fixed and the moving images into multiple feature map levels where a displacement field is estimated at each level and then further refined throughout the network. We train and test our model on three different datasets. In comparison with the initial registrations we find an improved performance using our model, yet we expect it would improve further if the model was fine-tuned for each task. The implementation is publicly available (https;//github.com/ngunnar/learning-a-deformable-registration-pyramid).
Recommended citation: Niklas Gunnarsson, Jens Sjölund, Thomas Schön, "Learning a Deformable Registration Pyramid." In the proceedings of Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data, 2021.
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