Semi-Supervised Retinal Vessel Segmentation Based on Pseudo Label Filtering

作者：Zheng Lü, Jiaguang Li, Zhenyu Liu, Qian Cao, Tao Tian, Xianchao Wang, Zou Huang · 发表于：Symmetry · 年份：2025 · DOI：10.3390/sym17091462 · 被引用次数：3 · 研究领域：Retinal Imaging and Analysis、Glaucoma and retinal disorders、Retinal Diseases and Treatments

Retinal vessel segmentation is crucial for analyzing medical images, where symmetry in vascular structures plays a fundamental role in diagnostic accuracy. In recent years, the rapid advancements in deep learning have provided robust tools for predicting detailed images. However, within many scenarios of medical image analysis, the task of data annotation remains costly and challenging to acquire. By leveraging symmetry-aware semi-supervised learning frameworks, our approach requires only a small portion of annotated data to achieve remarkable segmentation outcomes, significantly diminishing the costs associated with data labeling. At present, most semi-supervised approaches rely on pseudo-label update strategies. Nonetheless, while these methods generate high-quality pseudo-label images, they inevitably contain minor prediction errors in a few pixels, which can accumulate during iterative training, ultimately impacting learner performance. To address these challenges, we propose an enhanced semi-supervised vessel semantic segmentation approach that employs a symmetry-preserving pixel-level filtering strategy. This method retains highly reliable pixels in pseudo labels while eliminating those with low reliability, ensuring spatial symmetry coherence without altering the intrinsic spatial information of the images. The filtering strategy integrates various techniques, including probability-based filtering, edge detection, image filtering, mathematical morphology methods, and a...