# Optic Disc Segmentation using Disk-Centered Patch Augmentation

arXiv: https://arxiv.org/abs/2110.00512 · HTML: https://mlsanity.com/paper/2110.00512/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 1 Oct 2021.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Deep Retinal Image Understanding](https://mlsanity.com/paper/1609.01103/) (2016)
- [Dynamic Deep Networks for Retinal Vessel Segmentation](https://mlsanity.com/paper/1903.07803/) (2019)

## Built on by (most cited first, 2 of 2)

- [Fully Automated Tree Topology Estimation and Artery-Vein Classification](https://mlsanity.com/paper/2202.02382/) (2022)
- [Fully Automated Artery-Vein ratio and vascular tortuosity measurement in retinal fundus images](https://mlsanity.com/paper/2301.01791/) (2023)

## Abstract (arXiv)

The optic disc is a crucial diagnostic feature in the eye since changes to its physiognomy is correlated with the severity of various ocular and cardiovascular diseases. While identifying the bulk of the optic disc in a color fundus image is straightforward, accurately segmenting its boundary at the pixel level is very challenging. In this work, we propose disc-centered patch augmentation (DCPA) -- a simple, yet novel training scheme for deep neural networks -- to address this problem. DCPA achieves state-of-the-art results on full-size images even when using small neural networks, specifically a U-Net with only 7 million parameters as opposed to the original 31 million. In DCPA, we restrict the training data to patches that fully contain the optic nerve. In addition, we also train the network using dynamic cost functions to increase its robustness. We tested DCPA-trained networks on five retinal datasets: DRISTI, DRIONS-DB, DRIVE, AV-WIDE, and CHASE-DB. The first two had available optic disc ground truth, and we manually estimated the ground truth for the latter three. Our approach achieved state-of-the-art F1 and IOU results on four datasets (95 % F1, 91 % IOU on DRISTI; 92 % F1, 84 % IOU on DRIVE; 83 % F1, 71 % IOU on AV-WIDE; 83 % F1, 71 % IOU on CHASEDB) and competitive results on the fifth (95 % F1, 91 % IOU on DRIONS-DB), confirming its generality. Our open-source code and ground-truth annotations are available at: https://github.com/saeidmotevali/fundusdisk