Detail publikace

Simultaneous lesions and optic disc segmentation from ophthalmoscopic images

Originální název

Simultaneous lesions and optic disc segmentation from ophthalmoscopic images

Anglický název

Simultaneous lesions and optic disc segmentation from ophthalmoscopic images

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

Dokumenty

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"
}