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

**Giovana A. Benvenuto 1, Marilaine Colnago 2, Maurício A. Dias 1, Rogério G. Negri 3, Erivaldo A. Silva <sup>1</sup> and Wallace Casaca 4,\***


**Abstract:** 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 preannotated 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.

**Keywords:** fundus image; image registration; deep learning; computer vision applications
