Bioengineering (Aug 2022)

A Fully Unsupervised Deep Learning Framework for Non-Rigid Fundus Image Registration

  • Giovana A. Benvenuto,
  • Marilaine Colnago,
  • Maurício A. Dias,
  • Rogério G. Negri,
  • Erivaldo A. Silva,
  • Wallace Casaca

DOI
https://doi.org/10.3390/bioengineering9080369
Journal volume & issue
Vol. 9, no. 8
p. 369

Abstract

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In ophthalmology, the registration problem consists of finding a geometric transformation that aligns a pair of images, supporting eye-care specialists who need to record and compare images of the same patient. Considering the registration methods for handling eye fundus images, the literature offers only a limited number of proposals based on deep learning (DL), whose implementations use the supervised learning paradigm to train a model. Additionally, ensuring high-quality registrations while still being flexible enough to tackle a broad range of fundus images is another drawback faced by most existing methods in the literature. Therefore, in this paper, we address the above-mentioned issues by introducing a new DL-based framework for eye fundus registration. Our methodology combines a U-shaped fully convolutional neural network with a spatial transformation learning scheme, where a reference-free similarity metric allows the registration without assuming any pre-annotated or artificially created data. Once trained, the model is able to accurately align pairs of images captured under several conditions, which include the presence of anatomical differences and low-quality photographs. Compared to other registration methods, our approach achieves better registration outcomes by just passing as input the desired pair of fundus images.

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