Deep learning and ensemble method for optic disc and cup segmentation

Jongwoo Kim, Loc Tran, Tunde Peto, Emily Y. Chew

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Glaucoma is a chronic retinal disease that gradually damages the optic nerve. It is a leading cause of irreversible loss of vision. In ophthalmic fundus images, the cup to optic disc ratio measured around the optic nerve is a key measure used to screen for glaucomatous damages. Unfortunately, there is high subjectivity among ophthalmologists in estimating this ratio due to challenges in making reliable disc and cup measurements. To minimize this, we propose an automatic method using deep learning and ensemble method to segment the optic disc and cup. The proposed method comprises two steps. The region of interest (ROI), where optic disc is centered, is detected from a fundus image, following which the optic disc and cup are segmented from the ROI. Mask R-CNN algorithm is used to estimate the ROI, and two ensemble models based on three fully convolutional networks are used for the segmentation of optic disc and cup in parallel. The proposed method is trained and evaluated using the RIGA dataset that contains 750 fundus images and the REFUGE database containing 400 fundus images. The results demonstrate that the proposed method has a better performance compared with the current state-of-the-art algorithms. Our best segmentation results for optic disc shows 0.9303 Jaccard Index (JI) and 0.9635 Dice Coefficient (DC). The best segmentation results for cup shows 0.8096 JI and 0.8915 DC. The average cup to optic disc ratio error shows 0.0429.

Original languageEnglish
Title of host publication 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB): proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665484626
ISBN (Print)9781665484633
DOIs
Publication statusPublished - 26 Aug 2022
Event2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022 - Ottawa, Canada
Duration: 15 Aug 202217 Aug 2022

Publication series

NameIEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology: proceedings

Conference

Conference2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022
Country/TerritoryCanada
CityOttawa
Period15/08/202217/08/2022

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This research/work was supported in part by the Lister Hill National Center for Biomedical Communications of the National Library of Medicine (NLM), National Institutes of Health. We thank the MESSIDOR program, Magrabi Eye Center in Saudi Arabia and Bin Rushed Ophthalmic center in Saudi Arabia for providing the RIGA dataset. We thank the team of Jose Ignacio Orlando for providing REFUGE dataset. In addition, we acknowledge the help of the National Eye Institute staff in providing additional fundus images (AREDS dataset) to extend the current work.

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Cup
  • Deep Learning
  • Ensemble Method
  • Fully Convolutional Network (FCN)
  • Fundus Image
  • Glaucoma
  • Optic Disc

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Information Systems and Management
  • Computational Mathematics
  • Health Informatics

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