Simultaneous lesions and optic disc segmentation from ophthalmoscopic images
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F18%3APU129197" target="_blank" >RIV/00216305:26220/18:PU129197 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—
Alternative languages
Result language
angličtina
Original language name
Simultaneous lesions and optic disc segmentation from ophthalmoscopic images
Original language description
In this paper we present a novel approach to retina images segmentation. Simultaneously, 5 classes of objects are segmented including microaneurysms, haemorrhages, hard and soft exudates and optic disc. Segmentation of these eye disease symptoms is not straightforward, segmented objects are small, granular and may not be present in all images. We employ deep learning with fully convolutional methods. For a comparison, two different convolutional networks are used, SegNet and PSPNet. They are based on deep classifiers; therefore, we were able to use pretrained weights and only fine-tune both networks. Results suggest, we have chosen a perspective approach because we reached promising results.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
YBERC 2018 International Conference Proceedings
ISBN
978-80-8086-271-8
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
1-6
Publisher name
Katedra biomedicínskeho inžinierstva a merania
Place of publication
Kočice
Event location
Košice
Event date
Oct 3, 2018
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
—