Deep Learning-Based Multistep Deformable Medical Image Registration for Multimodal Minimal-Invasive Image-Guided Intervention

Motivation: We developed neural networks for deformable medical image registration using multiple steps and resolutions. Goal(s): To investigate how multiresolution networks impact registration results compared to monostep-monoresolution networks. Approach: The networks were trained unsupervised with Mutual Information and Gradient L2 loss. We compared them with a monoresolution-monostep network and the classical registration method SimpleElastix. We evaluated the multistep networks using a three-dimensional liver dataset with CT and T1-weighted MR scans. Results: Incorporating multiple steps and resolutions in the neural network yielded registration results with high spatial alignment and medically plausible transformations (minimal image folding) and fast registration times of less than half a second. Impact: Since the inclusion of multiple steps and resolutions within the neural network leads to improved registration results, multistep registration methods should be used whenever possible. Consequently, more work should be invested in developing multistep-multiresolution networks for multimodal medical image registration.