Optic disc and cup segmentation based on enhanced segnet

Lianyi Wu, Yiming Liu, Yelin Shi, Bin Sheng, Ping Li, Lei Bi, Jinman Kim

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Due to imbalanced distributed and restricted medical resources, reliable analysis for medical images is hard to come by, and it is impractical to only rely on human beings to do all the analysis, which is time-consuming and not economic. Application of computer vision techniques in such fields emerges as the situation requires. In this paper, we use deep learning segmentation algorithm to segment the optic disc and the cup from each other and from the rest of the ophthalmoscopy photographs. For a better performance, we change the loss function and crop as a way of data augmentation. The segmentation results can be used to calculate the cup-to-disc ratio (CDR), which is further used to diagnose glaucoma. Challenges such as over-fitting, biased dataset, and poor generalization of the model exist in front of us. We illustrate our model and associated methods dealing with these challenges.

Original languageEnglish
Title of host publicationProceedings of the 32nd International Conference on Computer Animation and Social Agents, CASA 2019
PublisherAssociation for Computing Machinery
Pages33-36
Number of pages4
ISBN (Electronic)9781450371599
DOIs
Publication statusPublished - 1 Jul 2019
Externally publishedYes
Event32nd International Conference on Computer Animation and Social Agents, CASA 2019 - Paris, France
Duration: 1 Jul 20193 Jul 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference32nd International Conference on Computer Animation and Social Agents, CASA 2019
Country/TerritoryFrance
CityParis
Period1/07/193/07/19

Keywords

  • Ophthalmoscopy
  • Optic disc and cup
  • Segmentation
  • SegNet

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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