A Deep Discontinuity-Preserving Image Registration Network

Xiang Chen; Yan Xia; Nishant Ravikumar; Alejandro F. Frangi

doi:10.1007/978-3-030-87202-1_5

A Deep Discontinuity-Preserving Image Registration Network

Xiang Chen^*, Yan Xia, Nishant Ravikumar, Alejandro F. Frangi

^*Corresponding author for this work

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
Editors	Marleen de Bruijne, Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
Publisher	Springer Nature
Pages	46-55
Number of pages	10
ISBN (Print)	9783030872014
DOIs	https://doi.org/10.1007/978-3-030-87202-1_5
Publication status	Published - 21 Sept 2021
Event	24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online Duration: 27 Sept 2021 → 1 Oct 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12904 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
City	Virtual, Online
Period	27/09/21 → 1/10/21

Keywords

Cardiac image registration
Deep learning
Discontinuity-preserving image registration
Image registration

Access to Document

10.1007/978-3-030-87202-1_5

Cite this

Chen, X., Xia, Y., Ravikumar, N., & Frangi, A. F. (2021). A Deep Discontinuity-Preserving Image Registration Network. In M. de Bruijne, M. de Bruijne, P. C. Cattin, S. Cotin, N. Padoy, S. Speidel, Y. Zheng, & C. Essert (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings (pp. 46-55). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12904 LNCS). Springer Nature. https://doi.org/10.1007/978-3-030-87202-1_5

Chen, Xiang ; Xia, Yan ; Ravikumar, Nishant et al. / A Deep Discontinuity-Preserving Image Registration Network. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings. editor / Marleen de Bruijne ; Marleen de Bruijne ; Philippe C. Cattin ; Stéphane Cotin ; Nicolas Padoy ; Stefanie Speidel ; Yefeng Zheng ; Caroline Essert. Springer Nature, 2021. pp. 46-55 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A Deep Discontinuity-Preserving Image Registration Network",

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.",

keywords = "Cardiac image registration, Deep learning, Discontinuity-preserving image registration, Image registration",

author = "Xiang Chen and Yan Xia and Nishant Ravikumar and Frangi, {Alejandro F.}",

note = "Funding Information: This research was conducted using the UKBB resource under access application 11350 and was sipported by the Royal Academy of Engineering under the RAEng Chair in Emerging Technologies (CiET1919/19) scheme and EPSRC TUSCA (EP/V04799X/1). Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 ; Conference date: 27-09-2021 Through 01-10-2021",

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Chen, X, Xia, Y, Ravikumar, N & Frangi, AF 2021, A Deep Discontinuity-Preserving Image Registration Network. in M de Bruijne, M de Bruijne, PC Cattin, S Cotin, N Padoy, S Speidel, Y Zheng & C Essert (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12904 LNCS, Springer Nature, pp. 46-55, 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online, 27/09/21. https://doi.org/10.1007/978-3-030-87202-1_5

A Deep Discontinuity-Preserving Image Registration Network. / Chen, Xiang; Xia, Yan; Ravikumar, Nishant et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings. ed. / Marleen de Bruijne; Marleen de Bruijne; Philippe C. Cattin; Stéphane Cotin; Nicolas Padoy; Stefanie Speidel; Yefeng Zheng; Caroline Essert. Springer Nature, 2021. p. 46-55 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12904 LNCS).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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T1 - A Deep Discontinuity-Preserving Image Registration Network

AU - Chen, Xiang

AU - Xia, Yan

AU - Ravikumar, Nishant

AU - Frangi, Alejandro F.

N1 - Funding Information: This research was conducted using the UKBB resource under access application 11350 and was sipported by the Royal Academy of Engineering under the RAEng Chair in Emerging Technologies (CiET1919/19) scheme and EPSRC TUSCA (EP/V04799X/1). Publisher Copyright: © 2021, Springer Nature Switzerland AG.

PY - 2021/9/21

Y1 - 2021/9/21

N2 - 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.

AB - 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.

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SN - 9783030872014

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings

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Chen X, Xia Y, Ravikumar N, Frangi AF. A Deep Discontinuity-Preserving Image Registration Network. In de Bruijne M, de Bruijne M, Cattin PC, Cotin S, Padoy N, Speidel S, Zheng Y, Essert C, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings. Springer Nature. 2021. p. 46-55. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-87202-1_5

A Deep Discontinuity-Preserving Image Registration Network

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Keywords

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