Deep Convolutional Artery/Vein Classification of Retinal Vessels

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2018

Autores

Maria Ines Meyer (Autor) Outra; Adrian Galdran (Autor) Outra; Pedro Costa (Autor) Outra; Ana Maria Mendonça (Autor) FEUP; Aurélio Campilho (Autor) FEUP

Ata de Conferência Internacional

Título: ICIAR

Páginas: 622-630

Indexação

Scopus - 24 Citações

INSPEC

Outras Informações

ID Authenticus: P-00N-ZM3

DOI: 10.1007/978-3-319-93000-8_71

Abstract (EN): The classification of retinal vessels into arteries and veins in eye fundus images is a relevant task for the automatic assessment of vascular changes. This paper presents a new approach to solve this problem by means of a Fully-Connected Convolutional Neural Network that is specifically adapted for artery/vein classification. For this, a loss function that focuses only on pixels belonging to the retinal vessel tree is built. The relevance of providing the model with different chromatic components of the source images is also analyzed. The performance of the proposed method is evaluated on the RITE dataset of retinal images, achieving promising results, with an accuracy of 96 % on large caliber vessels, and an overall accuracy of 84 %. © 2018, Springer International Publishing AG, part of Springer Nature.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9