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Updated: Jun 7, 2026

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Published on: March 26, 2020

DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation.

Bo Liu¹, Yudong Zhang², Shuihua Wang³

¹School of Information Engineering, Nanchang University, Nanchang, 330031, China.

Neural Networks : the Official Journal of the International Neural Network Society

|September 18, 2025

Summary

This study introduces a new method for retinal vessel segmentation, improving accuracy in diagnosing eye diseases. The approach enhances model generalization, leading to better performance across diverse imaging conditions.

Keywords:

Data augmentation Domain generalization Medical image segmentation Structural augmentation

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

Accurate retinal vessel segmentation is vital for diagnosing eye conditions like diabetic retinopathy, glaucoma, and hypertension.
Existing methods struggle with domain shifts, leading to reduced performance on varied imaging data.
Variations in imaging devices and patient demographics create challenges for robust retinal image analysis.

Purpose of the Study:

To develop a novel deep learning framework, DGSSA, for enhanced retinal vessel image segmentation.
To improve model generalization and robustness against domain shifts in retinal imaging.
To provide a more reliable tool for automated retinal vessel analysis in clinical settings.

Main Methods:

Proposed DGSSA framework combining structural and stylistic augmentation strategies.
Utilized a space colonization algorithm for generating diverse vascular structures and a Pix2Pix model for pseudo-retinal images.
Employed PixMix for photometric augmentations and uncertainty perturbations to enrich image style diversity.
Implemented a DeepLabv3+ model with a MobileNetV2 backbone for the segmentation network.

Main Results:

Achieved Dice Similarity Coefficients (DSC) of 78.45% (DRIVE), 78.62% (CHASEDB1), 72.66% (HRF), and 82.17% (STARE).
Attained an average DSC of 77.98% across four challenging datasets.
Demonstrated superior performance compared to existing retinal vessel segmentation approaches.

Conclusions:

The DGSSA framework significantly enhances retinal vessel segmentation generalization and robustness.
The combined structural and stylistic augmentation strategies effectively address domain shift issues.
The proposed method shows strong potential for clinical application in automated eye disease diagnosis.