Publication detail
Simultaneous lesions and optic disc segmentation from ophthalmoscopic images
HESKO, B. et al.
Original Title
Simultaneous lesions and optic disc segmentation from ophthalmoscopic images
English Title
Simultaneous lesions and optic disc segmentation from ophthalmoscopic images
Type
conference paper
Language
en
Original Abstract
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.
English abstract
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.
Keywords
deep learning, ophthalmology, retina images, segmentation
Released
05.10.2018
Publisher
Katedra biomedicínskeho inžinierstva a merania
Location
Kočice
ISBN
978-80-8086-271-8
Book
YBERC 2018 International Conference Proceedings
Pages from
1
Pages to
6
Pages count
6
BibTex
@inproceedings{BUT150379,
author="Branislav {Hesko} and Vratislav {Harabiš}",
title="Simultaneous lesions and optic disc segmentation from ophthalmoscopic images",
annote="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.",
address="Katedra biomedicínskeho inžinierstva a merania",
booktitle="YBERC 2018 International Conference Proceedings",
chapter="150379",
howpublished="electronic, physical medium",
institution="Katedra biomedicínskeho inžinierstva a merania",
year="2018",
month="october",
pages="1--6",
publisher="Katedra biomedicínskeho inžinierstva a merania",
type="conference paper"
}