# Tackling the Problem of Large Deformations in Deep Learning Based Medical Image Registration Using Displacement Embeddings Source: [Tackling the Problem of Large Deformations in Deep Learning Based Medical Image Registration Using Displacement Embeddings]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:18:12.808Z Authors: Lasse Hansen, Mattias P. Heinrich Published: 2020-05-27T13:06:24.000Z ## 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 registration accuracy. This is especially true for applications where large deformations exist, such as registration of interpatient abdominal MRI or inhale-to-exhale CT lung registration. 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). ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [3D registration on DirLab landmarks 3,000 expert-annotated (test)](): 3D registration; DirLab landmarks 3,000 expert-annotated (test); Average Error (mm): 3.42. ## Original sources and implementation [Original paper]() [PDF]()