Adversarial similarity network for evaluating image alignment in deep learning based registration

Jingfan Fan; Xiaohuan Cao; Zhong Xue; Pew Thian Yap; Dinggang Shen

doi:10.1007/978-3-030-00928-1_83

Adversarial similarity network for evaluating image alignment in deep learning based registration

Jingfan Fan
, Xiaohuan Cao
, Zhong Xue
, Pew Thian Yap
, Dinggang Shen^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

114 Citations (Scopus)

Abstract

This paper introduces an unsupervised adversarial similarity network for image registration. Unlike existing deep learning registration frameworks, our approach does not require ground-truth deformations and specific similarity metrics. We connect a registration network and a discrimination network with a deformable transformation layer. The registration network is trained with feedback from the discrimination network, which is designed to judge whether a pair of registered images are sufficiently similar. Using adversarial training, the registration network is trained to predict deformations that are accurate enough to fool the discrimination network. Experiments on four brain MRI datasets indicate that our method yields registration performance that is promising in both accuracy and efficiency compared with state-of-the-art registration methods, including those based on deep learning.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
Editors	Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger, Alejandro F. Frangi
Publisher	Springer Verlag
Pages	739-746
Number of pages	8
ISBN (Print)	9783030009274
DOIs	https://doi.org/10.1007/978-3-030-00928-1_83
Publication status	Published - 2018
Event	21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain Duration: 2018 Sept 16 → 2018 Sept 20

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11070 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Country/Territory	Spain
City	Granada
Period	18/9/16 → 18/9/20

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2018.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-00928-1_83

Cite this

Fan, J., Cao, X., Xue, Z., Yap, P. T., & Shen, D. (2018). Adversarial similarity network for evaluating image alignment in deep learning based registration. In J. A. Schnabel, C. Davatzikos, C. Alberola-López, G. Fichtinger, & A. F. Frangi (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings (pp. 739-746). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11070 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00928-1_83

Adversarial similarity network for evaluating image alignment in deep learning based registration. / Fan, Jingfan; Cao, Xiaohuan; Xue, Zhong et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. ed. / Julia A. Schnabel; Christos Davatzikos; Carlos Alberola-López; Gabor Fichtinger; Alejandro F. Frangi. Springer Verlag, 2018. p. 739-746 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11070 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Fan, J, Cao, X, Xue, Z, Yap, PT & Shen, D 2018, Adversarial similarity network for evaluating image alignment in deep learning based registration. in JA Schnabel, C Davatzikos, C Alberola-López, G Fichtinger & AF Frangi (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11070 LNCS, Springer Verlag, pp. 739-746, 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, Spain, 18/9/16. https://doi.org/10.1007/978-3-030-00928-1_83

Fan J, Cao X, Xue Z, Yap PT, Shen D. Adversarial similarity network for evaluating image alignment in deep learning based registration. In Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G, Frangi AF, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. Springer Verlag. 2018. p. 739-746. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-00928-1_83

Fan, Jingfan ; Cao, Xiaohuan ; Xue, Zhong et al. / Adversarial similarity network for evaluating image alignment in deep learning based registration. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. editor / Julia A. Schnabel ; Christos Davatzikos ; Carlos Alberola-López ; Gabor Fichtinger ; Alejandro F. Frangi. Springer Verlag, 2018. pp. 739-746 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Adversarial similarity network for evaluating image alignment in deep learning based registration

Abstract

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