<p>Optical coherence tomography angiography (OCTA) is a noninvasive retinal imaging technique. Precise segmentation of retinal vessels in OCTA images is crucial for the diagnosis of retinal pathologies and plays a key role in automated diagnostic systems. Moreover, accurate vessel segmentation enables reliable quantitative analysis, including vessel density, perfusion density, foveal avascular zone (FAZ) area, and vascular branching complexity. However, OCTA images often suffer from numerous artifacts and noise, which hinder accurate vessel segmentation. To overcome these challenges, we propose a novel dual-domain joint framework, termed DDNet. Specifically, DDNet incorporates three key modules: (1) a local aggregated state-space module, which enhances fine-grained perception by aggregating local contextual information; (2) an adaptive frequency enhancement module, which adaptively strengthens low-frequency and high-frequency components to suppress noise while preserving global structure and fine texture details; and (3) a wavelet-space adaptive fusion module, which adaptively integrates frequency-domain and spatial domain features to capture multi-domain vessel representations and improve vascular contrast. Extensive experiments demonstrate that DDNet achieves Dice coefficients of 0.8856, 0.9122, and 0.7682 on the OCTA500-3M, OCTA500-6M, and OCTA-RV datasets, respectively, outperforming state-of-the-art methods and highlighting its strong competitiveness.</p>