Abstract: Though, deep learning based medical image registration is currently starting to show promising advances, often, it still fells behind conventional frameworks in terms of reg- istration accuracy. This is especially true for applications where large deformations exist, such as registration of interpatient abdominal MRI or inhale-to-exhale CT lung registra- tion. Most current works use U-Net-like architectures to predict dense displacement fields from the input images in different supervised and unsupervised settings. We believe that the U-Net architecture itself to some level limits the ability to predict large deformations (even when using multilevel strategies) and therefore propose a novel approach, where the input images are mapped into a displacement space and final registrations are reconstructed from this embedding. Experiments on inhale-to-exhale CT lung registration demonstrate the ability of our architecture to predict large deformations in a single forward path through our network (leading to errors below 2 mm).
Paper Type: both
Track: short paper
Keywords: deformable image registration, convolutional neural networks, thoracic CT
TL;DR: state-of-the-art results for large deformations in deep learning based thoracic CT registration using embeddings of discrete feature displacements
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