Publication detail

Optical Disc Segmentation Using Fully Convolutional Neural Network in Retina Images

HESKO, B. KOLÁŘ, R. HARABIŠ, V.

Original Title

Optical Disc Segmentation Using Fully Convolutional Neural Network in Retina Images

English Title

Optical Disc Segmentation Using Fully Convolutional Neural Network in Retina Images

Type

conference paper

Language

en

Original Abstract

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.

English abstract

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.

Keywords

optic disc segmentation, deep learning, ophthalmology

Released

10.09.2018

Publisher

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

Location

Brno

ISBN

978-80-214-5661-7

Book

Proceedings of IEEE Student Branch Conference Blansko 2018

Edition

2018

Edition number

první

Pages from

16

Pages to

20

Pages count

4

BibTex


@inproceedings{BUT149748,
  author="Branislav {Hesko} and Radim {Kolář} and Vratislav {Harabiš}",
  title="Optical Disc Segmentation Using Fully Convolutional Neural Network in Retina Images",
  annote="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.",
  address="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií",
  booktitle="Proceedings of IEEE Student Branch Conference Blansko 2018",
  chapter="149748",
  edition="2018",
  howpublished="online",
  institution="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií",
  year="2018",
  month="september",
  pages="16--20",
  publisher="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií",
  type="conference paper"
}