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Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
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Unsupervised OCT Image Interpolation Using Deformable Registration and generative models.

Shuwen Wei1, Samuel W Remedios1,2, Zhangxing Bian1

  • 1Image Analysis and Communications Laboratory, Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|July 11, 2026
PubMed
Summary

This study introduces a novel unsupervised method for interpolating Optical Coherence Tomography (OCT) images, enhancing structural accuracy and realism. The new approach combines registration-based interpolation with deep generative models for superior OCT image analysis.

Keywords:
Deformable registrationGenerative modelImage interpolationOptical coherence tomographyUnsupervised learning

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Area of Science:

  • Medical imaging
  • Biomedical engineering
  • Computer vision

Background:

  • Optical Coherence Tomography (OCT) volumes are often anisotropic, with differing resolutions along fast and slow axes.
  • Anisotropy in OCT data complicates image analysis tasks like longitudinal registration.
  • Existing interpolation methods like bicubic and registration-based approaches have limitations in OCT image quality and realism.

Purpose of the Study:

  • To develop an unsupervised image interpolation method for OCT volumes.
  • To improve structural accuracy and realism in interpolated OCT images.
  • To overcome limitations of traditional interpolation techniques for anisotropic OCT data.

Main Methods:

  • Proposed an unsupervised interpolation method combining registration-based interpolation with a deep generative model.
  • Ensured structural consistency constraints were met during interpolation.
  • Evaluated the method on real OCT datasets.

Main Results:

  • The proposed method significantly improved interpolation performance compared to bicubic interpolation.
  • The new approach demonstrated superior realism and structural accuracy over registration-based interpolation.
  • Achieved the best interpolation performance on real OCT datasets.

Conclusions:

  • The novel unsupervised method effectively addresses the challenges of interpolating anisotropic OCT volumes.
  • This technique enhances the quality of OCT images for improved downstream analysis.
  • The combined approach offers a promising solution for realistic and structurally accurate OCT image interpolation.