A Single-Resolution Fully Convolutional Network for Retinal Vessel Segmentation in Raw Fundus Images

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2019

Autores

Ricardo J. Araújo (Autor) Outra; Jaime S. Cardoso (Autor) FEUP; Hélder P. Oliveira (Autor) FCUP

Ata de Conferência Internacional

Título: IMAGE ANALYSIS AND PROCESSING - ICIAP 2019, PT II

Páginas: 59-69

20th International Conference on Image Analysis and Processing (ICIAP)

Univ Trento, Fac Law, Trento, ITALY, SEP 09-13, 2019

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 2 Citações

Outras Informações

ID Authenticus: P-00R-0KZ

DOI: 10.1007/978-3-030-30645-8_6

Abstract (EN): The segmentation of retinal vessels in fundus images has been heavily focused in the past years, given their relevance in the diagnosis of several health conditions. Even though the recent advent of deep learning allowed to foster the performance of computer-based algorithms in this task, further improvement concerning the detection of vessels while suppressing background noise has clinical significance. Moreover, the best performing state-of-the-art methodologies conduct patch-based predictions. This, put together with the preprocessing techniques used in those methodologies, may hinder their use in screening scenarios. Thus, in this paper, we explore a fully convolutional setting that takes raw fundus images and allows to combine patch-based training with global image prediction. Our experiments on the DRIVE, STARE and CHASEDB1 databases show that the proposed methodology achieves state-of-the-art performance in the first and the last, allowing at the same time much faster segmentation of new images.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11