DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation
Journal:
arXiv
Published Date:
Jan 7, 2025
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.
