scholarly journals Retinal Vessel Segmentation Combined With Generative Adversarial Networks and Dense U-Net

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 194551-194560
Author(s):  
Xiaoyu Guo ◽  
Cheng Chen ◽  
Yuanyuan Lu ◽  
Ke Meng ◽  
Hongyu Chen ◽  
...  
Keyword(s):  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Retinal Vessel Segmentation

scholarly journals SERR-U-Net: Squeeze-and-Excitation Residual and Recurrent Block-Based U-Net for Automatic Vessel Segmentation in Retinal Image

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jinke Wang ◽  
Xiang Li ◽  
Peiqing Lv ◽  
Changfa Shi
Keyword(s):  
Retinal Image ◽  
Area Under The Curve ◽  
Super Resolution ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Retinal Vessel Segmentation ◽  
Block Based ◽  
Public Datasets

Background and Objective. Accurate segmentation of retinal vessels is considered as an important prerequisite for computer-aided diagnosis of ophthalmic diseases, diabetes, glaucoma, and other diseases. Although current learning-based methods have greatly improved the performance of retinal vessel segmentation, the accuracy could not meet the requirements of clinical assistance yet. Methods. A new SERR-U-Net framework for retinal vessel segmentation is proposed, which leverages technologies including Squeeze-and-Excitation (SE), residual module, and recurrent block. First, the convolution layers of encoder and decoder are modified on the basis of U-Net, and the recurrent block is used to increase the network depth. Second, the residual module is utilized to alleviate the vanishing gradient problem. Finally, to derive more specific vascular features, we employed the SE structure to introduce attention mechanism into the U-shaped network. In addition, enhanced super-resolution generative adversarial networks (ESRGANs) are also deployed to remove the noise of retinal image. Results. The effectiveness of this method was tested on two public datasets, DRIVE and STARE. In the experiment of DRIVE dataset, the accuracy and AUC (area under the curve) of our method were 0.9552 and 0.9784, respectively, and for SATRE dataset, 0.9796 and 0.9859 were achieved, respectively, which proved a high accuracy and promising prospect on clinical assistance. Conclusion. An improved U-Net network combining SE, ResNet, and recurrent technologies is developed for automatic vessel segmentation from retinal image. This new model enables an improvement on the accuracy compared to learning-based methods, and its robustness in circumvent challenging cases such as small blood vessels and intersection of vessels is also well demonstrated and validated.

A high resolution representation network with multi-path scale for retinal vessel segmentation

2021 ◽  
pp. 106206
Author(s):  
Zefang Lin ◽  
Jianping Huang ◽  
Yingyin Chen ◽  
Xiao Zhang ◽  
Wei Zhao ◽  
...  
Keyword(s):  
High Resolution ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Retinal Vessel Segmentation
Sign in / Sign up

Export Citation Format

Share Document