DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation
This addresses the critical need for robust automated retinal vessel analysis in diagnosing diseases like diabetes and glaucoma, though it appears incremental as it builds on existing augmentation and segmentation methods.
The paper tackles the problem of poor generalization in retinal vessel segmentation across different imaging domains by proposing DGSSA, which combines structural and style augmentation strategies. The approach achieved state-of-the-art performance on four challenging datasets (DRIVE, CHASEDB, HRF, and STARE), validating its effectiveness for clinical applications.
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.