EDGE-Net: Enhanced Distribution Gated Eye-Vessel Network for Ophthalmic Vascular Segmentation with Topological Constraints.
Authors
Affiliations (6)
Affiliations (6)
- School of Information Engineering, Yancheng Institute of Technology, Yancheng, Jiangsu, 224051, China.
- National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Key Laboratory of Genetic Evolution & Animal Models, and National Resource Center for Nonhuman Primates, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, 650221, China.
- National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an, Shaanxi, 710071, China.
- School of Resources and Environmental Engineering, Wuhan University of Science and Technology, Wuhan, Hubei, 430081, China.
- Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University, Xiamen, Fujian, 361005, China.
- College of Computer Engineering, Jimei University, Xiamen, Fujian, 361021, China. [email protected].
Abstract
Ophthalmic vascular segmentation supports quantitative analysis of the retinal vasculature, yet thin vessels, low contrast, and complex vascular topology remain challenging. We propose EDGE-Net (Enhanced Distribution Gated Eye-vessel Network), which combines the CLARITY preprocessing pipeline, a Dynamic Dual-Stream Attention Block (DDSAB), Multi-Stream Gated Feature Fusion (MSGFF), and topology-aware loss terms based on persistent homology and connectivity regularization. The framework is evaluated on three retinal vessel datasets (DRIVE, CHASE_DB1, and STARE) and two coronary angiography datasets (DCA1 and CHUAC); all hyperparameters are selected on a DRIVE validation split and then fixed for every dataset. EDGE-Net obtains the highest F1 score on all five datasets, ranging from 0.8394 to 0.8456 on the retinal datasets and from 0.7421 to 0.7518 on the coronary datasets, although it does not achieve the highest accuracy or specificity on every dataset. When the same dual-threshold iterative (DTI) post-processing is applied to every method on DRIVE, EDGE-Net obtains the lowest Vessel Connectivity Assessment score (3.31 versus 3.58 for the strongest competitor) and the highest Junction Preservation Rate (0.842 versus 0.812); both differences are significant after Holm correction (paired Wilcoxon signed-rank test, <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>p</mi> <mo><</mo> <mn>0.01</mn></mrow> </math> ). A controlled ablation attributes most of the pixel-level gain to DDSAB and MSGFF and most of the junction-preservation gain to the topology loss. Potential downstream uses include anatomically plausible vascular-network analysis; direct clinical benefit is not evaluated in this study.