DSDC-NET: Semi-supervised superficial OCTA vessel segmentation for false positive reduction

Abstract

Accurate vessel segmentation in Optical Coherence Tomography Angiography (OCTA) is essential for ocular disease diagnosis, monitoring, and treatment assessment. However, most current automatic segmentation methods overlook false positives in the segmentation results, leading to potential misdiagnosis and delayed treatment. To address this issue, we propose a Dynamic Spatial Semi-Supervised Vessel Segmentation with Dual Topological Consistency (DSDC-NET) for retinal superficial OCTA images. The network integrates a Dynamic Spatial Attention Mechanism that combines snake-shaped convolution, which captures tubular fine structures, with spatial attention to suppress background noise and artefacts. This design enhances vessel region responses while accurately capturing complex local structures, thereby reducing false positives arising from inaccurate localisation of vessel details. Furthermore, Dual Topological Consistency Loss integrates the Persistent Homology features of the vessel system with the topological skeleton features of major vessels, enhancing branching pattern recognition. A Warm-up mechanism balances the focus of the network between major and branch vessels across training phases, mitigating false positives from inadequate branching structure learning. Comprehensive evaluations on ROSE-1, OCTA-500, and ROSSA datasets demonstrate the superiority of DSDC-NET over existing methods. Notably, DSDC-NET effectively reduces the false discovery rate and improves segmentation accuracy, validating its effectiveness in reducing false positives.