A retrospective comparison of deep learning to manual annotations for optic disc and optic cup segmentation in fundus photographs

iChallenge-GON study group

Research output: Journal PublicationArticlepeer-review

2 Citations (Scopus)

Abstract

Purpose: Optic disc (OD) and optic cup (OC) segmentation are fundamental for fundus image analysis. Manual annotation is time consuming, expensive, and highly subjec-tive, whereas an automated system is invaluable to the medical community. The aim of this study is to develop a deep learning system to segment OD and OC in fundus photographs, and evaluate how the algorithm compares against manual annotations. Methods: A total of 1200 fundus photographs with 120 glaucoma cases were collected. The OD and OC annotations were labeled by seven licensed ophthalmologists, and glaucoma diagnoses were based on comprehensive evaluations of the subject medical records. A deep learning system for OD and OC segmentation was developed. The performances of segmentation and glaucoma discriminating based on the cup-to-disc ratio (CDR) of automated model were compared against the manual annotations. Results: The algorithm achieved an OD dice of 0.938 (95% confidence interval [CI] = 0.934–0.941), OC dice of 0.801 (95% CI = 0.793–0.809), and CDR mean absolute error (MAE) of 0.077 (95% CI = 0.073 mean absolute error (MAE)0.082). For glaucoma discriminating based on CDR calculations, the algorithm obtained an area under receiver operator characteristic curve (AUC) of 0.948 (95% CI = 0.920 mean absolute error (MAE)0.973), with a sensitivity of 0.850 (95% CI = 0.794–0.923) and specificity of 0.853 (95% CI = 0.798–0.918). Conclusions: We demonstrated the potential of the deep learning system to assist ophthalmologists in analyzing OD and OC segmentation and discriminating glaucoma from nonglaucoma subjects based on CDR calculations. Translational Relevance: We investigate the segmentation of OD and OC by deep learning system compared against the manual annotations.

Original languageEnglish
Article number33
Pages (from-to)1-11
Number of pages11
JournalTranslational Vision Science and Technology
Volume9
Issue number2
DOIs
Publication statusPublished - 2020
Externally publishedYes

Keywords

  • Artificial intelligence
  • Optic cup
  • Optic disc
  • Segmentation

ASJC Scopus subject areas

  • Biomedical Engineering
  • Ophthalmology

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