A Deep Learning Approach for Semantic Segmentation of Gonioscopic Images to Support Glaucoma Categorization

Andrea Peroni (Lead / Corresponding author), Carlo A. Cutolo, Luis A. Pinto, Anna Paviotti, Mauro Campigotto, Caroline Cobb, Jacintha Gong, Sirjhun Patel, Andrew Tatham, Stewart Gillan, Emanuele Trucco

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

Abstract

We present a deep learning semantic segmentation algorithm for processing images acquired by a novel ophthalmic device, the NIDEK GS-1. The proposed model can sophisticate the current reference exam, called gonioscopy, for evaluating the risk of developing glaucoma, a severe eye pathology with a considerable worldwide impact in terms of costs and negative effects on affected people’s quality of life, and for inferring its categorization. The target eye region of gonioscopy is the interface between the iris and the cornea, and the anatomical structures that are located there. Our approach exploits a dense U-net architecture and is the first automatic system segmenting irido-corneal interface images from the novel device. Results show promising performance, providing about 88% of mean pixel-wise classification accuracy in a 5-fold cross-validation experiment on a very limited size dataset of annotated images.

Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis - 24th Annual Conference, MIUA 2020, Proceedings
EditorsBartlomiej W. Papiez, Ana I. L. Namburete, Mohammad Yaqub, J. Alison Noble
Place of PublicationSwitzerland
PublisherSpringer
Pages373-386
Number of pages14
ISBN (Electronic)9783030527914
ISBN (Print)9783030527907
DOIs
Publication statusPublished - 2020
Event24th Annual Conference on Medical Image Understanding and Analysis, MIUA 2020 - Oxford, United Kingdom
Duration: 15 Jul 202017 Jul 2020

Publication series

NameCommunications in Computer and Information Science
Volume1248
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference24th Annual Conference on Medical Image Understanding and Analysis, MIUA 2020
CountryUnited Kingdom
CityOxford
Period15/07/2017/07/20

Keywords

  • Deep learning
  • Gonioscopy
  • Image segmentation

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