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

TL;DR

DGSSA introduces a novel augmentation framework combining structural and stylistic strategies to improve retinal vessel segmentation across diverse imaging conditions, outperforming existing methods.

Contribution

The paper proposes a new domain generalization approach, DGSSA, that uses vascular structure generation and style augmentation to enhance segmentation model robustness.

Findings

Achieved state-of-the-art results on four retinal datasets.

Enhanced model robustness to domain shifts and imaging variations.

Validated effectiveness for clinical retinal vessel analysis.

Abstract

Retinal vascular morphology is crucial for diagnosing diseases such as diabetes, glaucoma, and hypertension, making accurate segmentation of retinal vessels essential for early intervention. Traditional segmentation methods assume that training and testing data share similar distributions, which can lead to poor performance on unseen domains due to domain shifts caused by variations in imaging devices and patient demographics. This paper presents a novel approach, DGSSA, for retinal vessel image segmentation that enhances model generalization by combining structural and style augmentation strategies. We utilize a space colonization algorithm to generate diverse vascular-like structures that closely mimic actual retinal vessels, which are then used to generate pseudo-retinal images with an improved Pix2Pix model, allowing the segmentation model to learn a broader range of structure distributions. Additionally, we utilize PixMix to implement random photometric augmentations and introduce uncertainty perturbations, thereby enriching stylistic diversity and significantly enhancing the model's adaptability to varying imaging conditions. Our framework has been rigorously evaluated on four challenging datasets-DRIVE, CHASEDB, HRF, and STARE-demonstrating state-of-the-art performance that surpasses existing methods. This validates the effectiveness of our proposed approach, highlighting its potential for clinical application in automated retinal vessel analysis.