Frontiers in cell and developmental biology · 2026
Vision-language semantic guidance and topology refinement for robust retinal vessel segmentation and biomarker analysis in cerebral small vessel disease.
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Hua Shanshan, Chen Tao, Mi Yuwei, Shen Yu, Li Yisha, Zhang Jiong, Ji Yunxin
Аннотация
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.
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.