Chen, X orcid.org/0000-0003-4203-4578, Xia, Y, Ravikumar, N et al. (1 more author) (2021) A Deep Discontinuity-Preserving Image Registration Network. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part IV. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 27 Sep - 01 Oct 2021, Strasbourg, France/Virtual. Springer, Cham , pp. 46-55. ISBN 978-3-030-87202-1
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Nature Switzerland AG 2021. This is an author produced version of a conference paper published in Medical Image Computing and Computer Assisted Intervention – MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part IV. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Deep learning; Image registration; Cardiac image registration; Discontinuity-preserving image registration |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number Royal Academy of Engineering CiET1819\19 EPSRC (Engineering and Physical Sciences Research Council) EP/V04799X/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 03 Aug 2021 13:48 |
Last Modified: | 02 Dec 2021 22:51 |
Status: | Published |
Publisher: | Springer, Cham |
Identification Number: | 10.1007/978-3-030-87202-1_5 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:176533 |