Paper Info
Title
Segmentation Of Pigment Signs In Fundus Images For Retinitis Pigmentosa Analysis By Using Deep Learning
Abstract
The adoption of Deep Learning (DL) algorithms into the practice of ophthalmology could play an important role in screening and diagnosis of eye diseases in the coming years. In particular, DL tools interpreting ocular data derived from low-cost devices, as a fundus camera, could support massive screening also in resource limited countries. This paper explores a fully automatic method supporting the diagnosis of the Retinitis Pigmentosa by means of the segmentation of pigment signs in retinal fundus images. The proposed approach relies on an U-Net based deep convolutional network. At the present, this is the first approach for pigment signs segmentation in retinal fundus images that is not dependent on hand-crafted features, but automatically learns a hierarchy of increasingly complex features directly from data.We assess the performance by training the model on the public dataset RIPS and comparisons with the state of the art have been considered in accordance with approaches working on the same dataset. The experimental results show an improvement of 15% in F-measure score.
Year
DOI
Venue
2019
10.1007/978-3-030-30645-8_40
IMAGE ANALYSIS AND PROCESSING - ICIAP 2019, PT II
Keywords
DocType
Volume
Retinitis Pigmentosa, Pigment signs, Medical image analysis, Segmentation, Deep Learning
Conference
11752
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Nadia Brancati1297.76
Maria Frucci219026.24
Daniel Riccio317023.60
Luigi Di Perna411.04
Francesca Simonelli501.01