Pair-Wise and Group-Wise Deformation Consistency in Deep Registration Network

Dongdong Gu, Xiaohuan Cao, Shanshan Ma, Lei Chen, Guocai Liu, Dinggang Shen, Zhong Xue

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)


Ideally the deformation field from one image to another should be invertible and smooth to register images bidirectionally and preserve topology of anatomical structures. In traditional registration methods, differential geometry constraints could guarantee such topological consistency but are computationally intensive and time consuming. Recent studies showed that image registration using deep neural networks is as accurate as and also much faster than traditional methods. Current popular unsupervised learning-based algorithms aim to directly estimate spatial transformations by optimizing similarity between images under registration; however, the estimated deformation fields are often in one direction and do not possess inverse-consistency if swapping the order of two input images. Notice that the consistent registration can reduce systematic bias caused by the order of input images, increase robustness, and improve reliability of subsequent data analysis. Accordingly, in this paper, we propose a new training strategy by introducing both pair-wise and group-wise deformation consistency constraints. Specifically, losses enforcing both inverse-consistency for image pairs and cycle-consistency for image groups are proposed for model training, in addition to conventional image similarity and topology constraints. Experiments on 3D brain magnetic resonance (MR) images showed that such a learning algorithm yielded consistent deformations even after switching the order of input images or reordering images within groups. Furthermore, the registration results of longitudinal elderly MR images demonstrated smaller volumetric measurement variability in labeling regions of interest (ROIs).

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9783030597153
Publication statusPublished - 2020
Externally publishedYes
Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 2020 Oct 42020 Oct 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12263 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

Bibliographical note

Funding Information:
Acknowledgement. This work was partially supported by the National Key Research and Development Program of China (2018YFC0116400) and the National Natural Science Foundation of China (61671204).

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.


  • Cycle consistency
  • Deep learning
  • Inverse consistency
  • Medical image registration

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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