<![CDATA[Retinal blood vessel segmentation plays a crucial role in the early detection of ocular diseases such as diabetic retinopathy, glaucoma, and macular degeneration. Existing hybrid architectures, such as VGA-Net, suffer from high computational complexity due to the VGG-16 backbone and limited attention expressiveness due to its static GAT module, yet no prior work has examined replacing both components within a patch-based graph architecture in which backbone feature quality directly conditions graph attention effectiveness. This study aims to improve the computational efficiency and topological modeling of VGA-Net by replacing VGG-16 with ConvNeXt-Tiny and substituting GAT with GATv2. The primary contribution is a 55% parameter reduction through the ConvNeXt-Tiny backbone substitution and improved vessel topology modeling through GATv2's dynamic attention mechanism, which produces fully dynamic attention coefficients per query node. Experiments were conducted on the DRIVE and STARE datasets using a consistent preprocessing pipeline, one-factor-at-a-time hyperparameter tuning, and a unified evaluation protocol across all compared methods. The proposed model achieves the lowest parameter count (5.3M) and GFLOPs (3.2443), with a competitive inference time of 61.00 ms per image, among all compared methods, while achieving competitive performance in sensitivity and topological continuity. On the DRIVE dataset, the model achieved the highest sensitivity of 0.8718 and the highest clDice of 0.8446. On the STARE dataset, the model achieved the highest sensitivity of 0.9383 and the highest clDice of 0.9055. These results demonstrate that the proposed model achieves a favorable efficiency-performance trade-off, leading to sensitivity and topological continuity at the lowest computational cost among all compared methods, at the expense of lower specificity, accuracy, Dice, and MCC relative to certain compared methods.]]>
Copyrights © 2026
