Vision-language semantic guidance and topology refinement for robust retinal vessel segmentation and biomarker analysis in cerebral small vessel disease.

BACKGROUND

Retinal microvascular alterations observed in optical coherence tomography angiography (OCTA) have emerged as promising biomarkers for cerebral small vessel disease (CSVD). However, accurate quantification of OCTA-derived vascular biomarkers remains challenging due to imaging noise, projection artifacts, low contrast, and structural discontinuities, which often lead to unreliable vessel segmentation and distorted vascular topology.

PURPOSE: To address these challenges, we propose SemTopNet, a semantic-topology joint modeling framework for OCTA vessel segmentation.

METHODS

The proposed framework integrates vision-language semantic guidance, centerline-guided topology refinement, and uncertainty-aware adaptive supervision within a unified architecture. Specifically, semantic embeddings are incorporated to enhance global structural representation, while a centerline-guided topology refinement module explicitly improves vascular continuity and suppresses fragmented predictions. In addition, Monte Carlo dropout-based uncertainty estimation is employed to improve robustness in ambiguous and low signal-to-noise regions.

RESULTS

Extensive experiments on a CSVD OCTA dataset demonstrate that SemTopNet consistently outperforms existing convolutional and Transformer-based segmentation methods, achieving the best performance in Dice similarity coefficient (0.8931), clDice (0.9027), Recall (0.9293), and AUC (0.9567). Ablation studies further verify the effectiveness of semantic modeling, topology-aware refinement, and uncertainty-aware supervision. Based on the obtained segmentation results, quantitative retinal vascular biomarkers were further analyzed, revealing significant layer-dependent microvascular alterations in CSVD patients, particularly in the deep vascular complex (DVC).

CONCLUSION

These findings demonstrate that topology-consistent OCTA vessel segmentation is essential for reliable retinal microvascular quantification. The proposed SemTopNet provides a robust framework for OCTA biomarker extraction and retinal-cerebral vascular association analysis in CSVD-related studies.