Automated Retinal Image Analysis to Detect Optic Nerve Hypoplasia


  • Canan Celik Faculty of Informatics, Duzce University, Department of Electrical-Electronics and Computer Engineering, Duzce, Turkey
  • İbrahim Yücadag Faculty of Informatics; D Duzce University, Department of Computer Engineering, Duzce, Turkey
  • Hanife Tuba Akçam Faculty of Informatics; Department of Ophthalmology, Ankara Yıldırım Beyazıt University, Ankara, Turkey



Deep Learning, Image Segmentation, Optic Disc, Fovea, Macula, U-Net


Identification of the optic disc and fovea is crucial for automating the diagnosis and screening of retinal diseases. Based on quantitative calculations, this study presents a decision support system for doctors that automatically detect optic nerve hypoplasia. For disease diagnosis, U-Net architecture is used, which uses a pre-trained ResNet encoder to segment the optic disc and fovea structures. An important aspect of the proposed technique is that pretrained ResNet and U-Net are used together, providing robust performance in the detection of optic nerve hypoplasia. Our proposed architecture was tested on retinal images from Messidor, Diaretdb1, DRIVE, HRF, APTOS, and IDRID. In addition, a special database called ONH-NET was created based on 189 retinal images obtained from Düzce University, Department of Ophthalmology. Messidor database test images showed,
0. 9069 IOU Score, 0.9626 Sensitivity, 0.9411 Precision, 0.9974 Accuracy and 0.9505 dice-coefficient values in optic disc detection, and 0.8282 IOU score, 0.8442 sensitivity, 0.8252 precision, 0.8992 Accuracy, 0.7873 dice coefficient values were obtained in fovea detection. We computed diameter optic disc to macula radius ratios from segmented optic disc and fovea for screening optic nerve hypoplasia and achieved 100% success.