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

Autor(es)

Benvenuto, Giovana A. ; Colnago, Marilaine ; Dias, Mauricio A. ; Negri, Rogerio G. ; Silva, Erivaldo A. ; Casaca, Wallace

• Tipo de documento: Artigo Científico
• Fonte: BIOENGINEERING-BASEL; v. 9, n. 8, p. 17-pg., 2022-08-01.

Resumo

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. (AU)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 21/01305-6 - Avanços teóricos em detecção de anomalias e construção de sistemas de monitoramento ambiental
• Beneficiário: Rogério Galante Negri
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 19/26288-7 - Aprendizado não-supervisionado de registro de imagens de retina via redes neurais convolucionais e teoria do transporte ótimo de massa
• Beneficiário: Giovana Augusta Benvenuto
• Modalidade de apoio: Bolsas no Brasil - Mestrado
• Processo FAPESP: 21/03328-3 - Desenvolvimento de novas metodologias e soluções tecnológicas inteligentes em segmentação de imagens digitais e enfrentamento da COVID-19
• Beneficiário: Wallace Correa de Oliveira Casaca
• Modalidade de apoio: Auxílio à Pesquisa - Regular