Optical Disc Segmentation Using Fully Convolutional Neural Network in Retina Images
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F18%3APU128916" target="_blank" >RIV/00216305:26220/18:PU128916 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—
Alternative languages
Result language
angličtina
Original language name
Optical Disc Segmentation Using Fully Convolutional Neural Network in Retina Images
Original language description
This paper focuses on optic disc segmentation, which is one of the main steps in glaucoma diagnostics. A novel method, based on semantic, pixel-wise segmentation using the fully convolutional network is applied to the RIM-ONE dataset. This approach is advantageous because no additional preprocessing or postprocessing is needed. Moreover, results are promising, reaching mean IOU at about 0.7 and thus can compete with state of the art methods. The only disadvantage lays in the need of training dataset of sufficient size.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20204 - Robotics and automatic control
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
Proceedings of IEEE Student Branch Conference Blansko 2018
ISBN
978-80-214-5661-7
ISSN
—
e-ISSN
—
Number of pages
4
Pages from-to
16-20
Publisher name
Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Place of publication
Brno
Event location
Blansko
Event date
Sep 10, 2018
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
—