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

• Autor(a) principal: Benvenuto, Giovana A. [UNESP]
• Data de Publicação: 2022
• Outros Autores: Colnago, Marilaine, Dias, Maurício A. [UNESP], Negri, Rogério G. [UNESP], Silva, Erivaldo A. [UNESP], Casaca, Wallace [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.3390/bioengineering9080369; http://hdl.handle.net/11449/241615
• topic: computer vision applications; deep learning; fundus image; image registration

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.