mlsanityData to 7 Oct 2026

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

arXiv 2501.03466 ↗First posted 7 Jan 2025eess.IV cs.CVcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 37 of its 64 references that are in the map. First posted on arXiv 7 Jan 2025.

Builds on

37 of 64 references.

References · 37

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026ClinReadNet: A clinical reading-inspired network for low-dose abdominal CT image quality assessmentcited by 0
  2. 2026FSS-Net: Frequency-Spatial Synergy Network with Wavelet Attention for Carotid Artery Ultrasound Segmentationcited by 0