A Refined Equilibrium Generative Adversarial Network for Retinal Vessel Segmentation

Objective: Recognizing retinal vessel abnormity is vital to early diagnosis of ophthalmological diseases and cardiovascular events. However, segmentation results are highly influenced by elusive vessels, especially in low-contrast background and lesion region. In this work, we present an end-to-end synthetic neural network, containing a symmetric equilibrium generative adversarial network (SEGAN), multi-scale features refine blocks (MSFRB), and attention mechanism (AM) to enhance the performance on vessel segmentation. Method: The proposed network is granted powerful multi-scale representation capability to extract detail information. First, SEGAN constructs a symmetric adversarial architecture, which forces generator to produce more realistic images with local details. Second, MSFRB are devised to prevent high-resolution features from being obscured, thereby merging multi-scale features better. Finally, the AM is employed to encourage the network to concentrate on discriminative features. Results: On public dataset DRIVE, STARE, CHASEDB1, and HRF, we evaluate our network quantitatively and compare it with state-of-the-art works. The ablation experiment shows that SEGAN, MSFRB, and AM both contribute to the desirable performance. Conclusion: The proposed network outperforms the mature methods and effectively functions in elusive vessels segmentation, achieving highest scores in Sensitivity, G-Mean, Precision, and F1-Score while maintaining the top level in other metrics. Significance: The appreciable performance and computational efficiency offer great potential in clinical retinal vessel segmentation application. Meanwhile, the network could be utilized to extract detail information in other biomedical issues