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<p>Optical Coherence Tomography Angiography (OCTA) enables high-resolution visualization of the retinal vascular network. However, retinal vessels exhibit complex multi-scale and multi-directional patterns, particularly in thin vessels, bifurcations, and crossings. Accurate characterization of these structures is important for maintaining vascular connectivity and structural integrity, which are critical for reliable retinal vascular analysis and disease assessment. Existing methods often struggle to simultaneously capture directional continuity, long-range contextual dependencies, and fine-grained structural details, resulting in vessel discontinuities or mis-segmentation in small vessels and complex junction regions. To address these challenges, we propose an Adaptive Directional Feature Fusion Network (ADF-Net) for retinal vessel segmentation in OCTA images. Specifically, ADF-Net adopts a dual-branch encoder that integrates convolution-based local feature extraction and Swin Transformer-based global context modeling. Within this framework, a directional-aware multi-scale module is introduced with adaptive aggregation over fixed directional responses, modeling vascular structures in a 12-direction orientation space spanning 360 degrees. A direction-wise attention mechanism adaptively learns the relative importance of responses from different orientations and aggregates them accordingly, improving directional continuity in bifurcation and crossing regions. Multi-scale convolutions are incorporated to capture vessels of varying calibers, and an Edge Complement Module is introduced to explicitly refine vessel boundaries and reduce structural discontinuities. Experimental results on the OCTA-500 and ROSE-1 datasets demonstrate that the proposed method achieves competitive performance with several state-of-the-art approaches in retinal vessel segmentation. Specifically, on the OCTA-3&#xa0;M, OCTA-6&#xa0;M, and ROSE-1 datasets, ADF-Net achieves Dice coefficients of 91.51%, 88.88%, and 88.03%, and Intersection over Union (IoU) scores of 84.42%, 80.07%, and 78.61%, respectively. The results demonstrate that the proposed method effectively preserves vascular continuity and structural integrity for retinal vascular analysis and disease assessment. The source code of the proposed ADF-Net is publicly available at <a href="https://github.com/suxinli41-lgtm/ADF-Net">https://github.com/suxinli41-lgtm/ADF-Net</a>.</p>

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ADF-Net: Adaptive Directional Feature Fusion Network for OCTA Vessel Segmentation

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • Suxin Li,
  • Idowu Paul Okuwobi

摘要

Optical Coherence Tomography Angiography (OCTA) enables high-resolution visualization of the retinal vascular network. However, retinal vessels exhibit complex multi-scale and multi-directional patterns, particularly in thin vessels, bifurcations, and crossings. Accurate characterization of these structures is important for maintaining vascular connectivity and structural integrity, which are critical for reliable retinal vascular analysis and disease assessment. Existing methods often struggle to simultaneously capture directional continuity, long-range contextual dependencies, and fine-grained structural details, resulting in vessel discontinuities or mis-segmentation in small vessels and complex junction regions. To address these challenges, we propose an Adaptive Directional Feature Fusion Network (ADF-Net) for retinal vessel segmentation in OCTA images. Specifically, ADF-Net adopts a dual-branch encoder that integrates convolution-based local feature extraction and Swin Transformer-based global context modeling. Within this framework, a directional-aware multi-scale module is introduced with adaptive aggregation over fixed directional responses, modeling vascular structures in a 12-direction orientation space spanning 360 degrees. A direction-wise attention mechanism adaptively learns the relative importance of responses from different orientations and aggregates them accordingly, improving directional continuity in bifurcation and crossing regions. Multi-scale convolutions are incorporated to capture vessels of varying calibers, and an Edge Complement Module is introduced to explicitly refine vessel boundaries and reduce structural discontinuities. Experimental results on the OCTA-500 and ROSE-1 datasets demonstrate that the proposed method achieves competitive performance with several state-of-the-art approaches in retinal vessel segmentation. Specifically, on the OCTA-3 M, OCTA-6 M, and ROSE-1 datasets, ADF-Net achieves Dice coefficients of 91.51%, 88.88%, and 88.03%, and Intersection over Union (IoU) scores of 84.42%, 80.07%, and 78.61%, respectively. The results demonstrate that the proposed method effectively preserves vascular continuity and structural integrity for retinal vascular analysis and disease assessment. The source code of the proposed ADF-Net is publicly available at https://github.com/suxinli41-lgtm/ADF-Net.