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

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1248)


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.


Image segmentation Deep learning Gonioscopy 



We thank the CVIP/VAMPIRE research team of the University of Dundee, Dundee (UK) and the VAMPIRE research team of the University of Edinburgh, Edinburgh (UK) for useful discussions and suggestions.


This work is fully funded by a Ph.D. studentship from NIDEK Technologies Srl.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.VAMPIRE Project, Computing, School of Science and EngineeringUniversity of DundeeDundeeUK
  2. 2.Clinica Oculistica, DiNOGMIUniversity of GenoaGenoaItaly
  3. 3.Department of OphthalmologyHospital Santa MariaLisbonPortugal
  4. 4.NIDEK Technologies Srl.AlbignasegoItaly
  5. 5.Department of OphthalmologyNinewells Hospital, NHS TaysideDundeeUK
  6. 6.Department of OphthalmologyUniversity of DundeeDundeeUK
  7. 7.Princess Alexandra Eye Pavilion, NHS LothianEdinburghUK
  8. 8.OphthalmologyUniversity of EdinburghEdinburghUK

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