# Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network

arXiv: https://arxiv.org/abs/1704.00979 · HTML: https://mlsanity.com/paper/1704.00979/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2017.

## Built on by (most cited first, 16 of 16)

- [A multi-centre polyp detection and segmentation dataset for generalisability assessment](https://mlsanity.com/paper/2106.04463/) (2021)
- [CADA: Multi-scale Collaborative Adversarial Domain Adaptation for Unsupervised Optic Disc and Cup Segmentation](https://mlsanity.com/paper/2110.02417/) (2021)
- [A Study on the Use of Edge TPUs for Eye Fundus Image Segmentation](https://mlsanity.com/paper/2207.12770/) (2022)
- [Machine Learning-based Automatic Graphene Detection with Color Correction for Optical Microscope Images](https://mlsanity.com/paper/2103.13495/) (2021)
- [Dynamic region proposal networks for semantic segmentation in automated glaucoma screening](https://mlsanity.com/paper/2105.11364/) (2021)
- [CDRNet: Accurate Cup-to-Disc Ratio Measurement with Tight Bounding Box Supervision in Fundus Photography Using Deep Learning](https://mlsanity.com/paper/2110.00943/) (2021)
- [GARDNet: Robust Multi-View Network for Glaucoma Classification in Color Fundus Images](https://mlsanity.com/paper/2205.12902/) (2022)
- [Unsupervised Domain Adaptive Fundus Image Segmentation with Few Labeled Source Data](https://mlsanity.com/paper/2210.04379/) (2022)
- [UT-Net: Combining U-Net and Transformer for Joint Optic Disc and Cup Segmentation and Glaucoma Detection](https://mlsanity.com/paper/2303.04939/) (2023)
- [Energy efficiency in Edge TPU vs. embedded GPU for computer-aided medical imaging segmentation and classification](https://mlsanity.com/paper/2311.12876/) (2023)
- [MM-UNet: A Mixed MLP Architecture for Improved Ophthalmic Image Segmentation](https://mlsanity.com/paper/2408.08600/) (2024)
- [A comprehensive survey on computer-aided diagnostic systems in diabetic retinopathy screening](https://mlsanity.com/paper/2208.01810/) (2022)
- [Segmentation-based Information Extraction and Amalgamation in Fundus Images for Glaucoma Detection](https://mlsanity.com/paper/2209.11456/) (2022)
- [EDDense-Net: Fully Dense Encoder Decoder Network for Joint Segmentation of Optic Cup and Disc](https://mlsanity.com/paper/2308.10192/) (2023)
- [Innovative Quantitative Analysis for Disease Progression Assessment in Familial Cerebral Cavernous Malformations](https://mlsanity.com/paper/2403.15803/) (2024)
- [OptiModNet: A UNet-Transformer Hybrid with Grouped-Query and Channel Attention for Optic Disc and Cup Segmentation](https://mlsanity.com/paper/2608.18516/) (2026)

## Abstract (arXiv)

Glaucoma is the second leading cause of blindness all over the world, with approximately 60 million cases reported worldwide in 2010. If undiagnosed in time, glaucoma causes irreversible damage to the optic nerve leading to blindness. The optic nerve head examination, which involves measurement of cup-to-disc ratio, is considered one of the most valuable methods of structural diagnosis of the disease. Estimation of cup-to-disc ratio requires segmentation of optic disc and optic cup on eye fundus images and can be performed by modern computer vision algorithms. This work presents universal approach for automatic optic disc and cup segmentation, which is based on deep learning, namely, modification of U-Net convolutional neural network. Our experiments include comparison with the best known methods on publicly available databases DRIONS-DB, RIM-ONE v.3, DRISHTI-GS. For both optic disc and cup segmentation, our method achieves quality comparable to current state-of-the-art methods, outperforming them in terms of the prediction time.