# Robust Retinal Vessel Segmentation from a Data Augmentation Perspective

arXiv: https://arxiv.org/abs/2007.15883 · HTML: https://mlsanity.com/paper/2007.15883/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [Exploring The Limits Of Data Augmentation For Retinal Vessel Segmentation](https://mlsanity.com/paper/2105.09365/) (2021)
- [Orientation and Context Entangled Network for Retinal Vessel Segmentation](https://mlsanity.com/paper/2207.11396/) (2022)
- [Augmenting Medical Imaging: A Comprehensive Catalogue of 65 Techniques for Enhanced Data Analysis](https://mlsanity.com/paper/2303.01178/) (2023)
- [Self-Supervised Domain Adaptation for Diabetic Retinopathy Grading using Vessel Image Reconstruction](https://mlsanity.com/paper/2107.09372/) (2021)
- [Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation](https://mlsanity.com/paper/2211.06578/) (2022)
- [FS-Net: Full Scale Network and Adaptive Threshold for Improving Extraction of Micro-Retinal Vessel Structures](https://mlsanity.com/paper/2311.08059/) (2023)
- [Retinal Vessel Segmentation with Deep Graph and Capsule Reasoning](https://mlsanity.com/paper/2409.11508/) (2024)
- [Segmentation of Blood Vessels, Optic Disc Localization, Detection of Exudates and Diabetic Retinopathy Diagnosis from Digital Fundus Images](https://mlsanity.com/paper/2207.04345/) (2022)
- [Local Gamma Augmentation for Ischemic Stroke Lesion Segmentation on MRI](https://mlsanity.com/paper/2401.06893/) (2024)
- [Unsupervised Domain Adaptation for Retinal Vessel Segmentation with Adversarial Learning and Transfer Normalization](https://mlsanity.com/paper/2108.01821/) (2021)
- [L-MAE: Masked Autoencoders are Semantic Segmentation Datasets Augmenter](https://mlsanity.com/paper/2211.11242/) (2022)
- [Enhancing Community Vision Screening -- AI Driven Retinal Photography for Early Disease Detection and Patient Trust](https://mlsanity.com/paper/2410.20309/) (2024)

## Abstract (arXiv)

Retinal vessel segmentation is a fundamental step in screening, diagnosis, and treatment of various cardiovascular and ophthalmic diseases. Robustness is one of the most critical requirements for practical utilization, since the test images may be captured using different fundus cameras, or be affected by various pathological changes. We investigate this problem from a data augmentation perspective, with the merits of no additional training data or inference time. In this paper, we propose two new data augmentation modules, namely, channel-wise random Gamma correction and channel-wise random vessel augmentation. Given a training color fundus image, the former applies random gamma correction on each color channel of the entire image, while the latter intentionally enhances or decreases only the fine-grained blood vessel regions using morphological transformations. With the additional training samples generated by applying these two modules sequentially, a model could learn more invariant and discriminating features against both global and local disturbances. Experimental results on both real-world and synthetic datasets demonstrate that our method can improve the performance and robustness of a classic convolutional neural network architecture. The source code is available at urlhttps://github.com/PaddlePaddle/Research/tree/master/CV/robustvesselsegmentation.