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Adversarial learning for mono- or multi-modal registration.

Jingfan Fan1, Xiaohuan Cao1, Qian Wang2

  • 1Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Medical Image Analysis
|September 27, 2019
PubMed
Summary
This summary is machine-generated.

This study presents an unsupervised adversarial network for image registration, eliminating the need for ground-truth data. This novel approach achieves accurate and generalizable image registration for various modalities.

Keywords:
Deformable image registrationFully convolutional neural networkGenerative adversarial network

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Area of Science:

  • Medical image analysis
  • Computer vision
  • Machine learning

Background:

  • Image registration is crucial for medical image analysis, aligning images from different times or modalities.
  • Current deep learning methods often require extensive labeled data (ground-truth deformations) and specific similarity metrics, limiting their generalizability.
  • Developing unsupervised methods that can train deformable registration networks without ground-truth data is a significant challenge.

Purpose of the Study:

  • To introduce a novel unsupervised adversarial similarity network for image registration.
  • To enable training of deformable registration networks without relying on ground-truth deformations or predefined similarity metrics.
  • To establish a general framework applicable to both mono-modal and multi-modal image registration.

Main Methods:

  • An unsupervised adversarial similarity network architecture was developed, integrating a registration network and a discrimination network via a deformable transformation layer.
  • The registration network was trained adversarially, using feedback from the discrimination network to predict deformations that achieve sufficient image similarity.
  • The framework employs adversarial training to ensure the predicted deformations are accurate enough to deceive the discriminator.

Main Results:

  • The proposed method demonstrated promising performance across four brain MRI datasets and one multi-modal pelvic image dataset.
  • Experimental results indicate superior accuracy, efficiency, and generalizability compared to existing state-of-the-art registration methods, including deep learning-based approaches.
  • The unsupervised approach successfully trained deformable registration networks without ground-truth data.

Conclusions:

  • The unsupervised adversarial similarity network offers a powerful and generalizable solution for image registration.
  • This method overcomes limitations of supervised deep learning approaches by eliminating the need for ground-truth deformations.
  • The framework shows significant potential for advancing medical image analysis and other image registration applications.