A Deep Discontinuity-Preserving Image Registration Network
Xiang Chena✉, Yan Xiaa b, Nishant Ravikumara b, Alejandro Federico Frangia b c d
- aCenter for Computational Imaging and Simulation Technologies in Biomedicine, School of Computing, University of Leeds, Leeds, UK
- bBiomedical Imaging Department, Leeds Institute for Cardiovascular and Metabolic Medicine, School of Medicine University of Leeds, Leeds, UK
- cDepartment of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
- dDepartment of Electrical Engineering, KU Leuven, Leuven, Belgium
- ✉Corresponding author
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Abstract
Image registration aims to establish spatial correspondence across pairs, or groups of images, and is a cornerstone of medical image computing and computer-assisted-interventions. Currently, most deep learning-based registration methods assume that the desired deformation fields are globally smooth and continuous, which is not always valid for real-world scenarios, especially in medical image registration (e.g. cardiac imaging and abdominal imaging). Such a global constraint can lead to artefacts and increased errors at discontinuous tissue interfaces. To tackle this issue, we propose a weakly-supervised Deep Discontinuity-preserving Image Registration network (DDIR), to obtain better registration performance and realistic deformation fields. We demonstrate that our method achieves significant improvements in registration accuracy and predicts more realistic deformations, in registration experiments on cardiac magnetic resonance (MR) images from UK Biobank Imaging Study (UKBB), than state-of-the-art approaches.
TL;DRThis work proposes a weakly-supervised Deep Discontinuity-preserving Image Registration network (DDIR), and demonstrates that this method achieves significant improvements in registration accuracy and predicts more realistic deformations, in registration experiments on cardiac magnetic resonance (MR) images from UK Biobank Imaging Study (UKBB), than state-of-the-art approaches. (Semantic Scholar)