Robust Content-Adaptive Global Registration for Multimodal Retinal Images Using Weakly Supervised Deep-Learning Framework.

Journal: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Published Date: Feb 25, 2021

Abstract

Multimodal retinal imaging plays an important role in ophthalmology. We propose a content-adaptive multimodal retinal image registration method in this paper that focuses on the globally coarse alignment and includes three weakly supervised neural networks for vessel segmentation, feature detection and description, and outlier rejection. We apply the proposed framework to register color fundus images with infrared reflectance and fluorescein angiography images, and compare it with several conventional and deep learning methods. Our proposed framework demonstrates a significant improvement in robustness and accuracy reflected by a higher success rate and Dice coefficient compared with other methods.

Authors

  • Yiqian Wang
    Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
  • Junkang Zhang
    Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
  • Melina Cavichini
    Jacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, CA, USA.
  • Dirk-Uwe G Bartsch
    Jacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, CA, USA.
  • William R Freeman
    Jacobs Retina Center, Shiley Eye Institute, University of California San Diego, La Jolla, CA, USA.
  • Truong Q Nguyen
  • Cheolhong An
    Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.

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