Lightweight pyramid network with spatial attention mechanism for accurate retinal vessel segmentation

Purpose The morphological characteristics of retinal vessels are vital for the early diagnosis of pathological diseases such as diabetes and hypertension. However, the low contrast and complex morphology pose a challenge to automatic retinal vessel segmentation. To extract precise semantic features, more convolution and pooling operations are adopted, but some structural information is potentially ignored. Methods In the paper, we propose a novel lightweight pyramid network (LPN) fusing multi-scale features with spatial attention mechanism to preserve the structure information of retinal vessels. The pyramid hierarchy model is constructed to generate multi-scale representations, and its semantic features are strengthened with the introduction of the attention mechanism. The combination of multi-scale features contributes to its accurate prediction. Results The LPN is evaluated on benchmark datasets DRIVE, STARE and CHASE, and the results indicate its state-of-the-art performance (e.g., ACC of 97.09 % /97.49 % /97.48 % , AUC of 98.79 % /99.01 % /98.91 % on the DRIVE, STARE and CHASE datasets, respectively). The robustness and generalization ability of the LPN are further proved in cross-training experiment. Conclusion The visualization experiment reveals the semantic gap between various scales of the pyramid and verifies the effectiveness of the attention mechanism, which provide a potential basis for the pyramid hierarchy model in multi-scale vessel segmentation task. Furthermore, the number of model parameters is greatly reduced.