Unsupervised Image Registration towards Enhancing Performance and Explainability in Cardiac and Brain Image Analysis

Chengjia Wang, Guang Yang*, Giorgos Papanastasiou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
41 Downloads (Pure)


Magnetic Resonance Imaging (MRI) typically recruits multiple sequences (defined here as “modalities”). As each modality is designed to offer different anatomical and functional clinical in-formation, there are evident disparities in the imaging content across modalities. Inter‐ and intra-modality affine and non‐rigid image registration is an essential medical image analysis process in clinical imaging, as for example before imaging biomarkers need to be derived and clinically evaluated across different MRI modalities, time phases and slices. Although commonly needed in real clinical scenarios, affine and non‐rigid image registration is not extensively investigated using a single unsupervised model architecture. In our work, we present an unsupervised deep learning registration methodology that can accurately model affine and non‐rigid transformations, simulta-neously. Moreover, inverse‐consistency is a fundamental inter‐modality registration property that is not considered in deep learning registration algorithms. To address inverse consistency, our methodology performs bi‐directional cross‐modality image synthesis to learn modality‐invariant latent representations, and involves two factorised transformation networks (one per each encoder‐de-coder channel) and an inverse‐consistency loss to learn topology‐preserving anatomical transfor-mations. Overall, our model (named “FIRE”) shows improved performances against the reference standard baseline method (i.e., Symmetric Normalization implemented using the ANTs toolbox) on multi‐modality brain 2D and 3D MRI and intra‐modality cardiac 4D MRI data experiments. We focus on explaining model‐data components to enhance model explainability in medical image reg-istration. On computational time experiments, we show that the FIRE model performs on a memory‐saving mode, as it can inherently learn topology‐preserving image registration directly in the training phase. We therefore demonstrate an efficient and versatile registration technique that can have merit in multi‐modal image registrations in the clinical setting.

Original languageEnglish
Article number2125
Issue number6
Publication statusPublished - 9 Mar 2022


  • Deep learning
  • Explainable deep learning
  • Inverse‐consistency
  • Multi‐modality image registration
  • Unsupervised image registration

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering


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