DeepAI AI Chat
Log In Sign Up

A Symmetric Equilibrium Generative Adversarial Network with Attention Refine Block for Retinal Vessel Segmentation

by   Yukun Zhou, et al.

Objective: Recognizing retinal fundus vessel abnormity is vital to early diagnosis of ophthalmological diseases and cardiovascular events. However, segmentation results are highly influenced by elusive thin vessels. In this work, we present a synthetic network, including a symmetric equilibrium generative adversarial network (SEGAN), mul-ti-scale features refine blocks (MSFRB), and attention mechanism (AM) to enhance the performance on vessel segmentation especially for thin vessels. Method: The proposed network is granted powerful multi-scale repre-sentation capability. First, SEGAN is proposed to construct a symmetric adversarial architecture, which forces gener-ator to produce more realistic images with local details. Second, MSFRB are devised to prevent high-resolution features from being obscured, thereby preserving multi-scale features. Finally, the AM is employed to encourage the network to concentrate on discriminative features. Results: On public dataset DRIVE, STARE, and CHASEDB1, 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 of our network. Conclusion: The proposed network outperforms other strategies 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 per-formance and high computational efficiency offer great potential in clinical retinal vessel segmentation application. Meanwhile, the network could be utilized to extract detail information on other biomedical issues.


page 1

page 2

page 3

page 6

page 7

page 8


RV-GAN : Retinal Vessel Segmentation from Fundus Images using Multi-scale Generative Adversarial Networks

Retinal vessel segmentation contributes significantly to the domain of r...

Scale Space Approximation in Convolutional Neural Networks for Retinal Vessel Segmentation

Retinal images have the highest resolution and clarity among medical ima...

Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks

Retinal vessel segmentation is an indispensable step for automatic detec...

Superresolution and Segmentation of OCT scans using Multi-Stage adversarial Guided Attention Training

Optical coherence tomography (OCT) is one of the non-invasive and easy-t...

Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learning

Timely and affordable computer-aided diagnosis of retinal diseases is pi...

DPN: Detail-Preserving Network with High Resolution Representation for Efficient Segmentation of Retinal Vessels

Retinal vessels are important biomarkers for many ophthalmological and c...

(M)SLAe-Net: Multi-Scale Multi-Level Attention embedded Network for Retinal Vessel Segmentation

Segmentation plays a crucial role in diagnosis. Studying the retinal vas...