🤖 AI Summary
This work addresses the challenge of deploying high-accuracy retinal vessel segmentation models on resource-constrained devices, where computational overhead remains a significant barrier. To this end, the authors propose LightVesselNet, a lightweight encoder-decoder network containing only 75K parameters. The architecture innovatively integrates channel and spatial attention mechanisms, multi-scale feature aggregation, sub-pixel upsampling, and edge-aware residual connections. Despite its sub-100K parameter count, LightVesselNet achieves Dice coefficients ranging from 0.8070 to 0.8649 and sensitivities between 0.8096 and 0.8640 across five benchmark datasets—DRIVE, STARE, CHASE_DB1, FIVES, and HRF—demonstrating performance comparable to much larger models. These results significantly enhance the feasibility of edge deployment while exhibiting strong cross-dataset generalization capabilities.
📝 Abstract
Retinal blood vessel segmentation plays a vital role in the early detection of diabetic retinopathy and glaucoma. While recent deep learning models have achieved great segmentation accuracy, they typically require heavy computational resources, making real-world deployment on edge devices difficult. In this paper, we propose LightVesselNet, an efficient neural network designed for retinal vessel segmentation in a resource-constrained environment. Despite containing only 75K parameters, LightVesselNet performs competitively with much larger models. The network employs a compact encoder decoder architecture enhanced with channel and spatial attention mechanisms, a multi-scale feature aggregation module at the bottleneck, and a subpixel upsampling strategy in the decoder. A dedicated edge residual connection preserves fine vessel detail throughout decoding. Extensive experiments on five publicly available datasets: DRIVE, STARE, CHASEDB1, FIVES, and HRF, yield sensitivity scores of 0.8189, 0.8499, 0.8640, 0.8634, 0.8096, and Dice coefficients of 0.8070, 0.8072, 0.8181, 0.8649, and 0.7686, respectively. LightVesselNet shows improved efficiency (Performance vs Parameter or GFlops) compared to State-of-the-Art models. Cross-dataset evaluation confirms the model's generalisation capability. Overall, LightVesselNet is a strong candidate for deployment in low-resource clinical settings and mobile screening tools.