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

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.

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.

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[Formula: see text]/97.49[Formula: see text]/97.48[Formula: see text], AUC of 98.79[Formula: see text]/99.01[Formula: see text]/98.91[Formula: see text] on the DRIVE, STARE and CHASE datasets, respectively). The robustness and generalization ability of the LPN are further proved in cross-training experiment.

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.