DGSSA：面向视网膜血管分割的结构与风格增强领域泛化方法
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation
AI总结：
本文提出DGSSA方法，通过空间殖民算法生成类血管结构并结合Pix2Pix生成伪图像，同时利用PixMix光度增强和不确定性扰动提升风格多样性，在四个视网膜血管分割数据集上取得最先进性能。
摘要：
视网膜血管形态对于诊断糖尿病、青光眼和高血压等疾病至关重要，因此准确分割视网膜血管对于早期干预必不可少。传统分割方法假设训练数据和测试数据具有相似的分布，但由于成像设备和患者人口统计学差异引起的领域偏移，可能导致在未见领域上的性能不佳。本文提出了一种新颖的方法DGSSA，用于视网膜血管图像分割，通过结合结构和风格增强策略来提升模型泛化能力。我们利用空间殖民算法生成多样的类血管结构，这些结构紧密模拟真实视网膜血管，随后使用改进的Pix2Pix模型生成伪视网膜图像，使分割模型能够学习更广泛的结构分布。此外，我们利用PixMix实现随机光度增强，并引入不确定性扰动，从而丰富风格多样性，显著增强模型对不同成像条件的适应能力。我们的框架在四个具有挑战性的数据集——DRIVE、CHASEDB、HRF和STARE——上进行了严格评估，展示了超越现有方法的最先进性能。这验证了我们所提方法的有效性，突显了其在自动化视网膜血管分析中的临床应用潜力。
英文摘要：
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.
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