Share Email Print
cover

Proceedings Paper

Deep semantic segmentation of Diabetic Retinopathy lesions: what metrics really tell us
Author(s): Pedro Furtado
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Segmentation of lesions in eye fundus images (EFI) is a difficult problem, due to small sizes, varying morphologies, similarities and lack of contrast. Today, deep learning segmentation architectures are state-of-the-art in most segmentation tasks. But metrics need to be interpreted adequately to avoid wrong conclusions, e.g. we show that 90% global accuracy of the Fully Convolutional Network (FCN) does not mean it segments lesions very well. In this work we test and compare deep segmentation networks applied to find lesions in the Eye Fundus Images, focusing on comparison and how metrics really should be interpreted to avoid mistakes and why. In the light of this analysis, we finalize by discussing further challenges that lie ahead.

Paper Details

Date Published: 3 March 2020
PDF: 10 pages
Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 113170O (3 March 2020); doi: 10.1117/12.2549221
Show Author Affiliations
Pedro Furtado, Univ. de Coimbra (Portugal)


Published in SPIE Proceedings Vol. 11317:
Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Andrzej Krol; Barjor S. Gimi, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray